SpatialGym Dashboard

🔄 Turn 1

👤 Environment Observation (click to toggle)
# Spatial Exploration Task

You are a spatial reasoner in a 2D, text-only N×M grid. Every object including you is a point at integer (x, y) coordinates.

Goal: **Minimize total COST** while building a complete and accurate map of the environment.

Multi-room rules (may exist multiple rooms):
- Your ...

🤔 Assistant Thinking (click to toggle)
Start by observing from the initial pose (room 1, at origin, facing north) to discover visible objects/doors and their relations; no JumpTo allowed on the first step....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: No objects/doors observed; only agent pose is known.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "north" }
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: No objects or doors were observed in the current 90° FOV, so the local cognitive map contains an empty objects dictionary.
FINAL ANSWER: {
"origin": "agent",
"objects": {}
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {}
}

🎯 Ground Truth

(no data)

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {}
}

🎯 Ground Truth

(no data)

🧠 Cognitive Map Metrics

Global:

dir: 1

facing: 1

pos: 1

overall: 1

Global (Full):

dir: 0.008

facing: 0.100

pos: 0.067

overall: 0.058

Local:

(none)

Local (Newly):

(none)

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 0

edge_coverage: 0

is_action_fail: False

step: 1

action_counts:

move: 0

rotate: 0

return: 0

observe: 1

term: 0

forced_term: 0

query: 0

information_gain: 0

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 2

👤 Environment Observation (click to toggle)
You observe: No objects in field of view..
You have a maximum of 19 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
Nothing is visible facing north; rotate to scan a new 90° sector (east-facing) and observe again to locate any objects or doors in room 1....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Map agent at origin facing east after rotation; place airplane directly ahead at mid distance; place yellow door front-right at slightly far distance with southeast offset.
FINAL ANSWER:
```json
{
"agent": { "position": [0, 0], "facing": "east" },
"airplane": { "position": [3, 0], "facing...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      4,
      -4
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map with agent as origin; convert qualitative bearings/distances into consistent integer coordinates and encode the airplane’s facing as local +x (right).
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"airplane": {
"position": [0, 3],
"facin...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "airplane": {
      "position": [
        0,
        3
      ],
      "facing": "+x"
    },
    "yellow door": {
      "position": [
        4,
        4
      ]
    }
  }
}

🎯 Ground Truth

{
  "yellow door": {
    "position": [
      2,
      4
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      0,
      3
    ],
    "facing": "south"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "airplane": {
      "position": [
        0,
        3
      ],
      "facing": "+x"
    },
    "yellow door": {
      "position": [
        4,
        4
      ]
    }
  }
}

🎯 Ground Truth

{
  "yellow door": {
    "position": [
      2,
      4
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      0,
      3
    ],
    "facing": "south"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: Explored area is the agent’s current east-facing FOV containing the airplane and the yellow door region (near B/C/F). The lower-left region (A/D) and the corridor area (E) remain unobserved.
FINAL ANSWER: {"unexplored":["A","D","E"]}...

🗺️ Symbolic Fog Map

######################
#......########......#
#......+..*...#......#
#......#...B..#......#
#......#....CF+......#
#......#......#......#
#......#..DA..#......#
########.E....########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, D, E

🎯 Ground Truth: E, A, D

🧠 Cognitive Map Metrics

Global:

dir: 0.833

facing: 1

pos: 0.846

overall: 0.893

Global (Full):

dir: 0.042

facing: 0.200

pos: 0.169

overall: 0.137

Local:

dir: 0.667

facing: 1

pos: 0.815

overall: 0.827

Local (Newly):

dir: 0.667

facing: 1

pos: 0.815

overall: 0.827

Fog Probe:

precision: 1

recall: 1

overall: 1

📈 Turn Metrics

node_coverage: 0.143

edge_coverage: 0.049

is_action_fail: False

step: 2

action_counts:

move: 0

rotate: 1

return: 0

observe: 2

term: 0

forced_term: 0

query: 0

information_gain: 0.107

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 3

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• airplane: front, mid distance, facing right
• yellow door: front-right, slightly far, on front wall.
You have a maximum of 18 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
Jump to the observed yellow door to stand in the doorway; from there the door becomes open/invisible and I should be able to observe into both connected rooms, maximizing new information per Observe....

💬 Assistant Action
Actions: [JumpTo(yellow door), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assign integer coordinates consistent with observed egocentric bearings/distances; keep global frame with origin at initial agent position and agent currently at the yellow door, facing east.
FINAL ANSWER:
```json
{
"agent": { "position": [5, -3], "facing": "east" },
"yellow door": { "pos...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      5,
      -3
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      5,
      -3
    ]
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      8,
      -3
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      8,
      -5
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      10,
      -2
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map from the current observation; assign consistent integer coordinates matching the binned bearings/distances and the stated suitcase–truck proximity.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"truck": {
"position": [0, 3],
"facing": "+...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "truck": {
      "position": [
        0,
        3
      ],
      "facing": "+y"
    },
    "suitcase": {
      "position": [
        1,
        3
      ],
      "facing": "-x"
    },
    "backpack": {
      "position": [
        -1,
        6
      ],
      "facing": "-x"
    }
  }
}

🎯 Ground Truth

{
  "suitcase": {
    "position": [
      2,
      3
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      0,
      3
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      -2,
      5
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "truck": {
      "position": [
        0,
        3
      ],
      "facing": "+y"
    },
    "suitcase": {
      "position": [
        1,
        3
      ],
      "facing": "-x"
    },
    "backpack": {
      "position": [
        -1,
        6
      ],
      "facing": "-x"
    }
  }
}

🎯 Ground Truth

{
  "suitcase": {
    "position": [
      2,
      3
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      0,
      3
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      -2,
      5
    ],
    "facing": "north"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: Most observed area is the sector in front of the agent (to the right/east side near C/J/K/E). Points on the left side, behind, or in separated upper-right region remain unobserved.
FINAL ANSWER: {"unexplored":["A","B","D","F","G","H","I","L"]}...

🗺️ Symbolic Fog Map

######################
#......########GH....#
#......+.....A#..K...#
#......#..I.D.#.J....#
#......#.....L*...C..#
#......#..F...#......#
#......#.B....#E.....#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F, G, H, I, J, K, L

🤖 Predicted: A, B, D, F, G, H, I, L

🎯 Ground Truth: I, F, B, H, E, G

🧠 Cognitive Map Metrics

Global:

dir: 1

facing: 1

pos: 0.809

overall: 0.936

Global (Full):

dir: 0.175

facing: 0.500

pos: 0.323

overall: 0.333

Local:

dir: 0.667

facing: 1

pos: 0.866

overall: 0.844

Local (Newly):

dir: 0.667

facing: 1

pos: 0.866

overall: 0.844

Fog Probe:

precision: 0.625

recall: 0.833

overall: 0.714

📈 Turn Metrics

node_coverage: 0.357

edge_coverage: 0.122

is_action_fail: False

step: 3

action_counts:

move: 1

rotate: 1

return: 0

observe: 3

term: 0

forced_term: 0

query: 0

information_gain: 0.222

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 4

👤 Environment Observation (click to toggle)
You jumped to yellow door. You observe:
• suitcase: front-right, mid distance, facing left
• truck: front, mid distance, facing forward
• backpack: front-slight-left, slightly far, facing left
Assume your current facing direction is called "north":
• suitcase is east, near to truck.
You have a maxi...

🤔 Assistant Thinking (click to toggle)
Stay at the yellow doorway (maximizes cross-room visibility) and rotate to scan a different 90° sector; then observe to discover additional objects/possibly the other door....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assign consistent integer coordinates from relative bearings/distances across observations, keeping the initial pose as origin and global north as +y; update agent pose after moves/rotations and include only observed entities with cardinal facings where known.
FINAL ANSWER:
{
"agent": { "po...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      4,
      -4
    ]
  },
  "truck": {
    "position": [
      7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      6,
      -6
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      10,
      -2
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      2,
      -6
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local, agent-centered map from the current FOV; only the shelf is visible, positioned front-right at a mid-range distance.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"shelf": { "position": [2, 2] }
}
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shelf": {
      "position": [
        2,
        2
      ]
    }
  }
}

🎯 Ground Truth

{
  "shelf": {
    "position": [
      1,
      2
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shelf": {
      "position": [
        2,
        2
      ]
    }
  }
}

🎯 Ground Truth

{
  "shelf": {
    "position": [
      1,
      2
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: I’ve only observed around the yellow doorway area and a couple directions from it; the regions behind the other door and the far separated/top areas remain unobserved, while the immediate right/center-left candidates are more likely within already-seen sectors.
FINAL ANSWER:
{
"unexplored"...

🗺️ Symbolic Fog Map

######################
#......########DG....#
#......+I.F..H#L.....#
#......#......#......#
#......#..K...*..C...#
#......#......#......#
#......#...B..#A.J...#
########.....E########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F, G, H, I, J, K, L

🤖 Predicted: A, B, D, E, F, G, H, I, J, L

🎯 Ground Truth: I, K, B, D, G, L

🧠 Cognitive Map Metrics

Global:

dir: 0.786

facing: 1

pos: 0.753

overall: 0.846

Global (Full):

dir: 0.183

facing: 0.500

pos: 0.351

overall: 0.345

Local:

dir: 1

facing: 1

pos: 0.866

overall: 0.955

Local (Newly):

dir: 1

facing: 1

pos: 0.866

overall: 0.955

Fog Probe:

precision: 0.500

recall: 0.833

overall: 0.625

📈 Turn Metrics

node_coverage: 0.429

edge_coverage: 0.146

is_action_fail: False

step: 4

action_counts:

move: 1

rotate: 2

return: 0

observe: 4

term: 0

forced_term: 0

query: 0

information_gain: 0.260

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 5

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• shelf: front-right, mid distance.
You have a maximum of 16 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
Continue sweeping the view from the yellow doorway by rotating another 90° to cover a new sector and observe for more objects/doors....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single globally-consistent integer-coordinate map (origin at initial agent pose), approximating binned distances/angles; convert object “facing” from egocentric at observation time into global cardinal directions.
FINAL ANSWER:
{
"agent": { "position": [5, -2], "facing": "west"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      5,
      -2
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      5,
      -2
    ]
  },
  "truck": {
    "position": [
      8,
      -2
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      8,
      -4
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      10,
      -1
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      3,
      -5
    ]
  },
  "laptop": {
    "position": [
      0,
      -2
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      0,
      -4
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      0
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map using current FOV only; set approximate integer coordinates consistent with the binned bearings/distances, and encode object facings on local axes.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"bike": { "position": [-1, 5], "facing": "+x" }...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "bike": {
      "position": [
        -1,
        5
      ],
      "facing": "+x"
    },
    "laptop": {
      "position": [
        0,
        5
      ],
      "facing": "-x"
    },
    "brown door": {
      "position": [
        1,
        5
      ]
    }
  }
}

🎯 Ground Truth

{
  "bike": {
    "position": [
      -2,
      5
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      2,
      7
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      0,
      5
    ],
    "facing": "south"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "bike": {
      "position": [
        -1,
        5
      ],
      "facing": "+x"
    },
    "laptop": {
      "position": [
        0,
        5
      ],
      "facing": "-x"
    },
    "brown door": {
      "position": [
        1,
        5
      ]
    }
  }
}

🎯 Ground Truth

{
  "bike": {
    "position": [
      -2,
      5
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      2,
      7
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      0,
      5
    ],
    "facing": "south"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A, B, C, D, E, and F appear to lie in regions not yet directly observed (either behind walls/doors or outside the scanned FOV sectors), so they should be marked as unexplored.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

######################
#......########DC...F#
#......+......#E.....#
#......#......#....B.#
#......#......*......#
#......#......#...A..#
#......#......#......#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: D, E, C

🧠 Cognitive Map Metrics

Global:

dir: 0.891

facing: 1

pos: 0.848

overall: 0.913

Global (Full):

dir: 0.408

facing: 0.700

pos: 0.566

overall: 0.558

Local:

dir: 0.667

facing: 1

pos: 0.815

overall: 0.827

Local (Newly):

dir: 0.667

facing: 1

pos: 0.815

overall: 0.827

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.220

is_action_fail: False

step: 5

action_counts:

move: 1

rotate: 3

return: 0

observe: 5

term: 0

forced_term: 0

query: 0

information_gain: 0.362

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 6

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• bike: front-slight-left, slightly far, facing right
• laptop: front, slightly far, facing left
• brown door: front-slight-right, slightly far, on front wall
Assume your current facing direction is called "north":
• bike is west, near to laptop.
You have a m...

🤔 Assistant Thinking (click to toggle)
We’ve found the second door (brown door). Jump to it to stand in its doorway and observe; this should reveal the connected room (likely room 3) and remaining unseen objects....

💬 Assistant Action
Actions: [JumpTo(brown door), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a single consistent global placement (integer grid) for all observed objects/doors relative to initial origin, converting each observed facing from the agent’s orientation at that time into global cardinal directions.
FINAL ANSWER:
{
"agent": { "position": [0, -1], "facing": "west" },
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      -1
    ],
    "facing": "west"
  },
  "yellow door": {
    "position": [
      5,
      -3
    ]
  },
  "brown door": {
    "position": [
      0,
      -1
    ]
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      8,
      -3
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      8,
      -5
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      10,
      -1
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      3,
      -6
    ]
  },
  "laptop": {
    "position": [
      0,
      -3
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      0,
      -5
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      -3,
      -1
    ]
  },
  "cap": {
    "position": [
      -5,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -5,
      -4
    ]
  },
  "white chair": {
    "position": [
      -4,
      -4
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) integer-coordinate map consistent with the observed bearings/distances and the given near-relations (cap east of wine; white chair south of wine).
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"cap": {
"position": [
-1,
5
]...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        -1,
        5
      ],
      "facing": "+x"
    },
    "white chair": {
      "position": [
        -3,
        3
      ],
      "facing": "-y"
    },
    "wine": {
      "position": [
        -3,
        5
      ]
    },
    "pillow": {
      "position": [
        0,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "pillow": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  },
  "white chair": {
    "position": [
      -4,
      4
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -2,
      6
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -4,
      6
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        -1,
        5
      ],
      "facing": "+x"
    },
    "white chair": {
      "position": [
        -3,
        3
      ],
      "facing": "-y"
    },
    "wine": {
      "position": [
        -3,
        5
      ]
    },
    "pillow": {
      "position": [
        0,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "pillow": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  },
  "white chair": {
    "position": [
      -4,
      4
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -2,
      6
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -4,
      6
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: The candidates in the far-right room (A, D, F, G) and the top enclosed area (I, J) lie beyond the areas we have observed from the doorways, so they are in unexplored/unobserved regions.
FINAL ANSWER:
{
"unexplored": ["A", "D", "F", "G", "I", "J"]
}...

🗺️ Symbolic Fog Map

######################
#......########IJ..D.#
#..E...*......#A.....#
#......#......#......#
#...B..#......+.....G#
#L...CH#......#......#
#...K..#......#.....F#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F, G, H, I, J, K, L

🤖 Predicted: A, D, F, G, I, J

🎯 Ground Truth: H, C, K, I, J, A

🧠 Cognitive Map Metrics

Global:

dir: 0.857

facing: 1

pos: 0.688

overall: 0.848

Global (Full):

dir: 0.750

facing: 0.900

pos: 0.642

overall: 0.764

Local:

dir: 1

facing: 1

pos: 0.826

overall: 0.942

Local (Newly):

dir: 1

facing: 1

pos: 0.826

overall: 0.942

Fog Probe:

precision: 0.500

recall: 0.500

overall: 0.500

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.317

is_action_fail: False

step: 6

action_counts:

move: 2

rotate: 3

return: 0

observe: 6

term: 0

forced_term: 0

query: 0

information_gain: 0.468

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 7

👤 Environment Observation (click to toggle)
You jumped to brown door. You observe:
• cap: front-slight-left, slightly far, facing right
• white chair: front-left, slightly far, facing backward
• wine: front-left, slightly far
• pillow: front, mid distance
Assume your current facing direction is called "north":
• cap is east, near to wine
• w...

🤔 Assistant Thinking (click to toggle)
All but the alarmclock have been observed. From the brown doorway, do a 90° sector sweep by rotating and observing to locate the remaining object (and confirm no other unseen items in this room/adjacent room)....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a consistent integer-coordinate global map (origin at initial agent pose), using approximate placements that satisfy all observed egocentric bearings/distances and the reported proximity relations.
FINAL ANSWER:
```json
{
"agent": { "position": [0, -2], "facing": "north" },
"airpla...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      5,
      -3
    ]
  },
  "truck": {
    "position": [
      8,
      -3
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      8,
      -5
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      10,
      -2
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      3,
      -6
    ]
  },
  "laptop": {
    "position": [
      0,
      -3
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      0,
      -4
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      0,
      -2
    ]
  },
  "pillow": {
    "position": [
      -3,
      -2
    ]
  },
  "wine": {
    "position": [
      -5,
      -4
    ]
  },
  "cap": {
    "position": [
      -5,
      -3
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -4,
      -4
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Current FOV contains no observed objects or doors, so the local map is empty relative to the agent.
FINAL ANSWER:
{
"origin": "agent",
"objects": {}
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {}
}

🎯 Ground Truth

(no data)

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {}
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Large portions of the left-side areas (B, J, E, F, I, G) and the right-side compartments beyond walls/doorway (D, H, K, L, A, C) have not been directly observed in our FOV sweeps, so they remain unexplored.
FINAL ANSWER:
{"unexplored":["A","B","C","D","E","F","G","H","I","J","K","L"]}...

🗺️ Symbolic Fog Map

######################
#..B...########DH....#
#......*......#KL....#
#.J....#......#.A....#
#......#......+......#
#.E..FI#......#...C..#
#....G.#......#......#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F, G, H, I, J, K, L

🤖 Predicted: A, B, C, D, E, F, G, H, I, J, K, L

🎯 Ground Truth: I, F, G, K, H, D

🧠 Cognitive Map Metrics

Global:

dir: 0.829

facing: 0.889

pos: 0.672

overall: 0.797

Global (Full):

dir: 0.725

facing: 0.800

pos: 0.627

overall: 0.717

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.317

is_action_fail: False

step: 7

action_counts:

move: 2

rotate: 4

return: 0

observe: 7

term: 0

forced_term: 0

query: 0

information_gain: 0.468

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 8

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe: No objects in field of view..
You have a maximum of 13 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
No objects in this sector; continue the sweep around from the brown doorway by rotating another 90° and observing to find the alarmclock (and ensure full coverage)....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single globally consistent integer-coordinate map from all binned observations, anchoring the agent’s initial pose at origin and using doorway observations to triangulate approximate positions/facings while prioritizing mutual consistency across repeated sightings.
FINAL ANSWER: {...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ]
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      2,
      -5
    ]
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ]
  },
  "cap": {
    "position": [
      -8,
      -1
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -8,
      -3
    ]
  },
  "white chair": {
    "position": [
      -6,
      -3
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -6,
      0
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) JSON map including only currently visible items, placing them at integer coordinates consistent with the reported bearing/distance bins; include facing for objects that have it.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"airplane": {
"position...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "airplane": {
      "position": [
        0,
        5
      ],
      "facing": "+x"
    },
    "laptop": {
      "position": [
        2,
        3
      ],
      "facing": "+x"
    },
    "shelf": {
      "position": [
        4,
        4
      ]
    },
    "yellow door": {
      "position": [
        2,
        5
      ]
    }
  }
}

🎯 Ground Truth

{
  "shelf": {
    "position": [
      4,
      6
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      0,
      6
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      2,
      2
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      2,
      7
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "airplane": {
      "position": [
        0,
        5
      ],
      "facing": "+x"
    },
    "laptop": {
      "position": [
        2,
        3
      ],
      "facing": "+x"
    },
    "shelf": {
      "position": [
        4,
        4
      ]
    },
    "yellow door": {
      "position": [
        2,
        5
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The left-side room (separated by walls from my doorway-based views) remains unobserved; candidate points there are in unexplored regions.
FINAL ANSWER:
```json
{
"unexplored": ["A", "C", "F", "G", "J", "K"]
}
```...

🗺️ Symbolic Fog Map

######################
#......########BL....#
#K.....*......#I.....#
#......#......#.H....#
#...F.C#......+....DE#
#..G...#......#......#
#...AJ.#......#......#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F, G, H, I, J, K, L

🤖 Predicted: A, C, F, G, J, K

🎯 Ground Truth: C, A, J, I, B, L

🧠 Cognitive Map Metrics

Global:

dir: 0.962

facing: 1

pos: 0.885

overall: 0.949

Global (Full):

dir: 0.842

facing: 0.900

pos: 0.826

overall: 0.856

Local:

dir: 0.900

facing: 1

pos: 0.796

overall: 0.899

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 0.500

overall: 0.500

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.390

is_action_fail: False

step: 8

action_counts:

move: 2

rotate: 5

return: 0

observe: 8

term: 0

forced_term: 0

query: 0

information_gain: 0.805

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 9

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• airplane: front, slightly far, facing right
• laptop: front-right, mid distance, facing right
• shelf: front-right, slightly far
• yellow door: front-slight-right, slightly far, on front wall.
You have a maximum of 12 exploration steps left.
Strictly follow...

🤔 Assistant Thinking (click to toggle)
Still haven’t seen the alarmclock; continue the systematic 90° sweep from the brown doorway by rotating another 90° and observing the next sector....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK:
Need a single consistent global placement (integer grid) for all observed items/doors relative to the initial origin, plus the agent’s current pose. I’ll assign coordinates that best satisfy the observed egocentric bearings/distances and the stated proximity relations, keeping the map self-co...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      4,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      5,
      -2
    ]
  },
  "suitcase": {
    "position": [
      10,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      8,
      -2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      11,
      -1
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      3,
      -4
    ]
  },
  "bike": {
    "position": [
      0,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      0,
      -2
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      -2,
      1
    ]
  },
  "cap": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -6,
      -5
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -6,
      -3
    ]
  },
  "pillow": {
    "position": [
      -5,
      1
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: I will encode each visible item’s relative location using integer offsets consistent with its binned egocentric direction (front-left/front-right) and distance bin (mid vs slightly-far), and express facing using local axes (+y forward, +x right).
FINAL ANSWER: {
"origin": "agent",
"object...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "bike": {
      "position": [
        -2,
        5
      ],
      "facing": "-y"
    },
    "white chair": {
      "position": [
        2,
        5
      ],
      "facing": "-x"
    },
    "laptop": {
      "position": [
        -1,
        3
      ],
      "facing": "+y"
    }
  }
}

🎯 Ground Truth

{
  "white chair": {
    "position": [
      4,
      4
    ],
    "facing": "east"
  },
  "bike": {
    "position": [
      -2,
      4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -2,
      2
    ],
    "facing": "south"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "bike": {
      "position": [
        -2,
        5
      ],
      "facing": "-y"
    },
    "white chair": {
      "position": [
        2,
        5
      ],
      "facing": "-x"
    },
    "laptop": {
      "position": [
        -1,
        3
      ],
      "facing": "+y"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A, B, C, D, E, and F all lie in the separated right-side area beyond the visible doorway/wall boundary, which corresponds to an unobserved/unexplored region relative to the explored space around the agent.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

######################
#......########EB....#
#......*......#D.....#
#......#......#...C.F#
#......#......+......#
#......#......#......#
#......#......#....A.#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: B, E, D

🧠 Cognitive Map Metrics

Global:

dir: 0.771

facing: 1

pos: 0.763

overall: 0.845

Global (Full):

dir: 0.675

facing: 0.900

pos: 0.712

overall: 0.762

Local:

dir: 0.167

facing: 1

pos: 0.790

overall: 0.652

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.415

is_action_fail: False

step: 9

action_counts:

move: 2

rotate: 6

return: 0

observe: 9

term: 0

forced_term: 0

query: 0

information_gain: 0.837

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 10

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• bike: front-left, slightly far, facing backward
• white chair: front-right, slightly far, facing left
• laptop: front-left, mid distance, facing forward
Assume your current facing direction is called "north":
• bike is north, near to laptop.
You have a maxi...

🤔 Assistant Thinking (click to toggle)
One sector remains unscanned from this position; rotate 90° again and observe to try to pick up the alarmclock (or confirm it’s elsewhere/out of view from this doorway)....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need an integer-coordinate global map consistent with all qualitative observations; place objects relative to initial origin and maintain consistent facings inferred from “facing left/right/forward/backward” at time of observation.
FINAL ANSWER:
{
"agent": { "position": [0, -1], "facing": "...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      -1
    ],
    "facing": "west"
  },
  "yellow door": {
    "position": [
      5,
      -3
    ]
  },
  "brown door": {
    "position": [
      0,
      -1
    ]
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      8,
      -3
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      8,
      -4
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      12,
      -1
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      2,
      -6
    ]
  },
  "laptop": {
    "position": [
      0,
      -3
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      0,
      -5
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      -3,
      -1
    ]
  },
  "cap": {
    "position": [
      -5,
      -3
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -5,
      -4
    ]
  },
  "white chair": {
    "position": [
      -4,
      -4
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map using only currently visible items, assigning integer coordinates consistent with the reported bearing/distance bins and the given proximity relations.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"cap": {
"position": [
-1,
6
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        -1,
        6
      ],
      "facing": "+x"
    },
    "white chair": {
      "position": [
        -3,
        4
      ],
      "facing": "-y"
    },
    "wine": {
      "position": [
        -3,
        6
      ]
    },
    "pillow": {
      "position": [
        0,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "pillow": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  },
  "white chair": {
    "position": [
      -4,
      4
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -2,
      6
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -4,
      6
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        -1,
        6
      ],
      "facing": "+x"
    },
    "white chair": {
      "position": [
        -3,
        4
      ],
      "facing": "-y"
    },
    "wine": {
      "position": [
        -3,
        6
      ]
    },
    "pillow": {
      "position": [
        0,
        3
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The agent is confined to the central-left region; all candidate points A-F lie beyond separating walls/doorways (right-side and upper-right compartments), so they are in unexplored/unobserved regions.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

######################
#......########CE....#
#......*......#B....D#
#......#......#......#
#......#......+...F..#
#......#......#......#
#......#......#.....A#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: E, B, C

🧠 Cognitive Map Metrics

Global:

dir: 0.829

facing: 1

pos: 0.676

overall: 0.835

Global (Full):

dir: 0.725

facing: 0.900

pos: 0.631

overall: 0.752

Local:

dir: 0.900

facing: 1

pos: 0.866

overall: 0.922

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.415

is_action_fail: False

step: 10

action_counts:

move: 2

rotate: 7

return: 0

observe: 10

term: 0

forced_term: 0

query: 0

information_gain: 0.837

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 11

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• cap: front-slight-left, slightly far, facing right
• white chair: front-left, slightly far, facing backward
• wine: front-left, slightly far
• pillow: front, mid distance
Assume your current facing direction is called "north":
• cap is east, near to wine
• ...

🤔 Assistant Thinking (click to toggle)
The only unseen object is the alarmclock; a viewpoint change is needed. Jump to a central previously seen object (truck) to get a different vantage and observe a new 90° slice for the alarmclock....

💬 Assistant Action
Actions: [JumpTo(truck), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a consistent integer-coordinate global map (origin at initial agent pose), using observed bearings/distances plus the reported proximity constraints to place objects/doors; include only observed items and cardinal facings where given.
FINAL ANSWER:
{
"agent": { "position": [-2, -2], "f...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      -2
    ],
    "facing": "west"
  },
  "yellow door": {
    "position": [
      4,
      -4
    ]
  },
  "brown door": {
    "position": [
      -2,
      -2
    ]
  },
  "airplane": {
    "position": [
      4,
      -1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -6
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      10,
      -2
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      2,
      -6
    ]
  },
  "laptop": {
    "position": [
      0,
      -4
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      0,
      -6
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -8,
      -5
    ]
  },
  "cap": {
    "position": [
      -7,
      -5
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -8,
      -6
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -5,
      -2
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map using integer coordinates consistent with the observed egocentric direction bins, distance bins, and stated proximity relations (cap east-near wine; white chair south-near wine).
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"cap": { "positi...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        -1,
        6
      ],
      "facing": "+x"
    },
    "wine": {
      "position": [
        -3,
        6
      ]
    },
    "white chair": {
      "position": [
        -3,
        4
      ],
      "facing": "-y"
    },
    "pillow": {
      "position": [
        0,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "pillow": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  },
  "white chair": {
    "position": [
      -4,
      4
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -2,
      6
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -4,
      6
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        -1,
        6
      ],
      "facing": "+x"
    },
    "wine": {
      "position": [
        -3,
        6
      ]
    },
    "white chair": {
      "position": [
        -3,
        4
      ],
      "facing": "-y"
    },
    "pillow": {
      "position": [
        0,
        3
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidates A-F all lie in map areas that have not been directly observed/covered yet from our explored doorways, especially the far-right and top-right regions behind walls/around the door, so they are likely unexplored.
FINAL ANSWER: {"unexplored":["A","B","C","D","E","F"]}...

🗺️ Symbolic Fog Map

######################
#......########EA....#
#......*......#F.....#
#......#......#......#
#......#......+....D.#
#......#......#......#
#......#......#C..B..#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: F, E, A

🧠 Cognitive Map Metrics

Global:

dir: 0.819

facing: 1

pos: 0.721

overall: 0.847

Global (Full):

dir: 0.717

facing: 0.900

pos: 0.673

overall: 0.763

Local:

dir: 0.900

facing: 1

pos: 0.866

overall: 0.922

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.415

is_action_fail: True

step: 11

action_counts:

move: 2

rotate: 7

return: 0

observe: 11

term: 0

forced_term: 0

query: 0

information_gain: 0.837

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 12

👤 Environment Observation (click to toggle)
Cannot jump to 'truck': object not visible. Subsequent actions are skipped due to failure, instead an observe is executed: You observe:
• cap: front-slight-left, slightly far, facing right
• white chair: front-left, slightly far, facing backward
• wine: front-left, slightly far
• pillow: front, mid...

🤔 Assistant Thinking (click to toggle)
Truck isn’t currently in FOV, so switch viewpoint by jumping to a currently visible object (pillow) inside this room; then observe from there to try to reveal the remaining unseen alarmclock....

💬 Assistant Action
Actions: [JumpTo(pillow), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single global integer-coordinate map consistent with all qualitative observations/jumps; include only observed entities and assign one plausible fixed facing per object where reported.
FINAL ANSWER:
{
"agent": { "position": [-5, -2], "facing": "west" },
"airplane": { "position": ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -5,
      -2
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      4,
      -4
    ]
  },
  "suitcase": {
    "position": [
      7,
      -6
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      7,
      -4
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      10,
      -2
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      2,
      -6
    ]
  },
  "bike": {
    "position": [
      0,
      -7
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      0,
      -5
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      -2,
      -2
    ]
  },
  "cap": {
    "position": [
      -6,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -4,
      -6
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -6,
      -6
    ]
  },
  "pillow": {
    "position": [
      -5,
      -2
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local, agent-centric map from the current FOV; place the cap at an integer front-left mid-distance coordinate and encode its facing as +x (right).
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"cap": {
"position": [-2, 2],
"facing": "+x"
}
}
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        -2,
        2
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "cap": {
    "position": [
      -2,
      2
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        -2,
        2
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Candidate points A-F all lie in the right-side/top-right/lower-right areas that have not been directly observed/covered from our visited viewpoints, so they are in unexplored regions.
FINAL ANSWER:
{"unexplored":["A","B","C","D","E","F"]}...

🗺️ Symbolic Fog Map

######################
#......########ED....#
#..*...+......#A.B...#
#......#......#.....C#
#......#......+......#
#......#......#......#
#......#......#F.....#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: D, E, A

🧠 Cognitive Map Metrics

Global:

dir: 0.771

facing: 1

pos: 0.677

overall: 0.816

Global (Full):

dir: 0.675

facing: 0.900

pos: 0.632

overall: 0.736

Local:

dir: 1

facing: 1

pos: 1

overall: 1

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.439

is_action_fail: False

step: 12

action_counts:

move: 3

rotate: 7

return: 0

observe: 12

term: 0

forced_term: 0

query: 0

information_gain: 0.844

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 13

👤 Environment Observation (click to toggle)
You jumped to pillow. You observe:
• cap: front-left, mid distance, facing right.
You have a maximum of 8 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
From the pillow position, start a new 4-sector sweep (90° increments) to search for the alarmclock; rotate to a different sector and observe....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a globally consistent integer-coordinate map using the agent’s initial pose as origin, tracking jumps/rotations and satisfying the observed binned relations as constraints.
FINAL ANSWER:
{
"agent": { "position": [-4, -1], "facing": "north" },
"airplane": { "position": [3, 0], "f...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -4,
      -1
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      6,
      -3
    ]
  },
  "truck": {
    "position": [
      9,
      -3
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      9,
      -5
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      11,
      -2
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      5,
      -6
    ]
  },
  "laptop": {
    "position": [
      1,
      -3
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      1,
      -5
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -1
    ]
  },
  "pillow": {
    "position": [
      -4,
      -1
    ]
  },
  "wine": {
    "position": [
      -6,
      -5
    ]
  },
  "cap": {
    "position": [
      -6,
      -3
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -4,
      -5
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: No objects are currently within my 90° field of view after rotating; the local cognitive map should therefore contain an empty objects dictionary.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {}
}
```...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {}
}

🎯 Ground Truth

(no data)

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {}
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Candidate points A, C, D, E, and B lie in the far/right-side areas that have not been directly observed yet; F appears adjacent to the known doorway region and is likely within already observed space.
FINAL ANSWER: {
"unexplored": ["A", "B", "C", "D", "E"]
}...

🗺️ Symbolic Fog Map

######################
#......########AC.D..#
#..*...+......#E.....#
#......#......#......#
#......#......+F.....#
#......#......#.....B#
#......#......#......#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E

🎯 Ground Truth: C, A, E

🧠 Cognitive Map Metrics

Global:

dir: 0.914

facing: 1

pos: 0.685

overall: 0.867

Global (Full):

dir: 0.800

facing: 0.900

pos: 0.640

overall: 0.780

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 0.600

recall: 1

overall: 0.750

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.439

is_action_fail: False

step: 13

action_counts:

move: 3

rotate: 8

return: 0

observe: 13

term: 0

forced_term: 0

query: 0

information_gain: 0.844

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 14

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe: No objects in field of view..
You have a maximum of 7 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
Continue the sector sweep from the pillow position; rotate another 90° to scan the next quadrant for the alarmclock and observe....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will output an approximate global cognitive map (integer grid) consistent with the observed relative bearings/distances and jump targets; include only observed objects/doors and the agent with global cardinal facing.
FINAL ANSWER:
```json
{
"agent": { "position": [-4, 5], "facing": "east...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -4,
      5
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      5,
      3
    ]
  },
  "brown door": {
    "position": [
      -1,
      5
    ]
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "suitcase": {
    "position": [
      8,
      1
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      8,
      3
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      11,
      5
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      3,
      1
    ]
  },
  "bike": {
    "position": [
      -1,
      1
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      3
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -5,
      1
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -7,
      1
    ]
  },
  "pillow": {
    "position": [
      -4,
      5
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local map from the current FOV only; brown door is straight ahead at a mid distance, so place it on +y with an integer offset.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"brown door": { "position": [0, 3] }
}
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "brown door": {
      "position": [
        0,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "brown door": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "brown door": {
      "position": [
        0,
        3
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A, B, C (right-side lower area) and D, E, F (right-side upper area) lie in regions that have not been fully observed from our visited viewpoints, so they remain unexplored.
FINAL ANSWER:
{"unexplored":["A","B","C","D","E","F"]}...

🗺️ Symbolic Fog Map

######################
#......########FD....#
#..*...+......#E.....#
#......#......#......#
#......#......+.B....#
#......#......#...A..#
#......#......#...C..#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: E, F, D

🧠 Cognitive Map Metrics

Global:

dir: 0.705

facing: 1

pos: 0.478

overall: 0.728

Global (Full):

dir: 0.617

facing: 0.900

pos: 0.446

overall: 0.654

Local:

dir: 1

facing: 1

pos: 0.866

overall: 0.955

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.439

is_action_fail: False

step: 14

action_counts:

move: 3

rotate: 9

return: 0

observe: 14

term: 0

forced_term: 0

query: 0

information_gain: 0.844

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 15

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• brown door: front, mid distance, on front wall.
You have a maximum of 6 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
From the pillow position, continue the 90° sweep to cover the next quadrant; rotate 90° and observe to search for the missing alarmclock (and potentially bring other objects like truck/suitcase into view for a jump)....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single consistent global (x,y) map from the binned egocentric observations across viewpoints, converting object “facing” from egocentric to global cardinal directions based on the agent orientation at first observation; include only observed entities plus the agent’s current pose....

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      4,
      -3
    ]
  },
  "truck": {
    "position": [
      7,
      -3
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -5
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      10,
      -1
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      3,
      -5
    ]
  },
  "laptop": {
    "position": [
      0,
      -3
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      0,
      -4
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -1
    ]
  },
  "wine": {
    "position": [
      -6,
      -4
    ]
  },
  "cap": {
    "position": [
      -6,
      -2
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -4,
      -4
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -4,
      -1
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) map using only objects currently in FOV, assigning consistent integer coordinates that match the reported bearings/distances and nearby relations.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"cap": {
"position": [2, 3],
"facing": ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        2,
        3
      ],
      "facing": "-y"
    },
    "white chair": {
      "position": [
        0,
        4
      ],
      "facing": "-x"
    },
    "wine": {
      "position": [
        2,
        4
      ]
    }
  }
}

🎯 Ground Truth

{
  "white chair": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      2,
      2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      2,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        2,
        3
      ],
      "facing": "-y"
    },
    "white chair": {
      "position": [
        0,
        4
      ],
      "facing": "-x"
    },
    "wine": {
      "position": [
        2,
        4
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The agent is in the left-side region; all candidate points A-F lie in separated right-side compartments beyond walls/doors that have not been directly observed/covered, so they are in unexplored/unobserved regions.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

######################
#......########CB.F..#
#..*...+......#D.....#
#......#......#..A...#
#......#......+..E...#
#......#......#......#
#......#......#......#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: D, B, C

🧠 Cognitive Map Metrics

Global:

dir: 0.886

facing: 1

pos: 0.740

overall: 0.875

Global (Full):

dir: 0.775

facing: 0.900

pos: 0.691

overall: 0.789

Local:

dir: 1

facing: 1

pos: 0.920

overall: 0.973

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.488

is_action_fail: False

step: 15

action_counts:

move: 3

rotate: 10

return: 0

observe: 15

term: 0

forced_term: 0

query: 0

information_gain: 0.885

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 16

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• cap: front-right, mid distance, facing backward
• white chair: front, mid distance, facing left
• wine: front-right, slightly far
Assume your current facing direction is called "north":
• cap is south, near to wine
• white chair is west, near to wine.
You h...

🤔 Assistant Thinking (click to toggle)
One quadrant from the pillow position still hasn’t been scanned in this sweep; rotate 90° again and observe to try to spot the alarmclock (or any object that gives a better jump vantage)....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a single consistent global (initial-origin) integer-coordinate map using only observed items/doors; approximate coordinates from egocentric bearings + binned distances, keep agent final pose at pillow, include facing only where reported (converted to global cardinals).
FINAL ANSWER:
{
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -5,
      -3
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      4,
      -4
    ]
  },
  "suitcase": {
    "position": [
      7,
      -6
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      7,
      -4
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      10,
      -3
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      2,
      -6
    ]
  },
  "bike": {
    "position": [
      -2,
      -5
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -2,
      -4
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      -2,
      -3
    ]
  },
  "cap": {
    "position": [
      -7,
      -6
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -6,
      -7
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -8,
      -7
    ]
  },
  "pillow": {
    "position": [
      -5,
      -3
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map from the current FOV only. Agent faces +y; the cap is front-left at mid distance, so place it at an integer front-left coordinate consistent with that bin and include its facing.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"cap": {
"position...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        -2,
        3
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "cap": {
    "position": [
      -2,
      2
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        -2,
        3
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The missing alarmclock and limited FOV sweeps suggest several room corners/segments weren’t directly observed; all labeled candidates appear placed in separate, likely-unseen pockets across the middle/right/top areas.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

######################
#......########BF....#
#..*...+......#D.....#
#......#......#......#
#......#......+..C..E#
#......#......#.....A#
#......#......#......#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: B, F, D

🧠 Cognitive Map Metrics

Global:

dir: 0.771

facing: 1

pos: 0.667

overall: 0.813

Global (Full):

dir: 0.675

facing: 0.900

pos: 0.623

overall: 0.733

Local:

dir: 1

facing: 1

pos: 0.866

overall: 0.955

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.488

is_action_fail: False

step: 16

action_counts:

move: 3

rotate: 11

return: 0

observe: 16

term: 0

forced_term: 0

query: 0

information_gain: 0.885

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 17

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• cap: front-left, mid distance, facing right.
You have a maximum of 4 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
From the pillow position, the brown door was previously visible when facing the opposite direction; rotate 180° to bring that sector back into view and observe (also a chance to spot the alarmclock)....

💬 Assistant Action
Actions: [Rotate(180), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK:
Construct a single global map (origin at initial agent pose) using a consistent integer placement that fits the majority of the observed relative bearings/distances and proximity constraints; include only observed items and add facing for items where it was reported.
FINAL ANSWER:
{
"agent"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -4
    ]
  },
  "brown door": {
    "position": [
      -2,
      0
    ]
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -6
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      10,
      -2
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      2,
      -6
    ]
  },
  "laptop": {
    "position": [
      -2,
      -4
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      -2,
      -6
    ],
    "facing": "north"
  },
  "cap": {
    "position": [
      -8,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -8,
      -4
    ]
  },
  "white chair": {
    "position": [
      -6,
      -4
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -5,
      0
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Only the brown door is currently in FOV; place it directly ahead at an integer position consistent with “mid distance”.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"brown door": {
"position": [0, 3]
}
}
}
```...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "brown door": {
      "position": [
        0,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "brown door": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "brown door": {
      "position": [
        0,
        3
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Most of the environment beyond the doors (especially the right-side rooms and the upper-right enclosed area) remains unobserved; all labeled candidate points appear to lie in regions not yet covered by our FOV observations.
FINAL ANSWER:
{"unexplored":["A","B","C","D","E","F"]}...

🗺️ Symbolic Fog Map

######################
#......########BA....#
#..*...+......#D.....#
#......#......#......#
#......#......+....E.#
#......#......#......#
#......#......#..C.F.#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: B, A, D

🧠 Cognitive Map Metrics

Global:

dir: 0.810

facing: 1

pos: 0.774

overall: 0.861

Global (Full):

dir: 0.708

facing: 0.900

pos: 0.723

overall: 0.777

Local:

dir: 1

facing: 1

pos: 0.866

overall: 0.955

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.488

is_action_fail: False

step: 17

action_counts:

move: 3

rotate: 12

return: 0

observe: 17

term: 0

forced_term: 0

query: 0

information_gain: 0.885

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 18

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• brown door: front, mid distance, on front wall.
You have a maximum of 3 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
We still haven’t found the alarmclock. From the pillow position I can currently see the brown door, so I’ll jump back to the brown doorway to regain cross-room visibility; then next step I can rotate to bring the yellow door/truck area into view for a jump....

💬 Assistant Action
Actions: [JumpTo(brown door), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single global map anchored at the initial agent origin, assigning integer coordinates consistent with the observed relative bearings/distances and jump locations; include only objects/doors that were observed and include facing where reported.
FINAL ANSWER:
```json
{
"agent": {...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      -2
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      4,
      -4
    ]
  },
  "suitcase": {
    "position": [
      6,
      -6
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      7,
      -4
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      10,
      -2
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      2,
      -6
    ]
  },
  "bike": {
    "position": [
      -1,
      -7
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -2,
      -4
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      -2,
      -2
    ]
  },
  "cap": {
    "position": [
      -8,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -5
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -8,
      -5
    ]
  },
  "pillow": {
    "position": [
      -5,
      -2
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) map using representative integer coordinates consistent with the observed bearing/distance bins; include only items currently in FOV and facing where provided.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"airplane": { "position": [0, 6], "fac...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "airplane": {
      "position": [
        0,
        6
      ],
      "facing": "+x"
    },
    "laptop": {
      "position": [
        2,
        2
      ],
      "facing": "+x"
    },
    "shelf": {
      "position": [
        4,
        4
      ]
    },
    "yellow door": {
      "position": [
        2,
        6
      ]
    }
  }
}

🎯 Ground Truth

{
  "shelf": {
    "position": [
      4,
      6
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      0,
      6
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      2,
      2
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      2,
      7
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "airplane": {
      "position": [
        0,
        6
      ],
      "facing": "+x"
    },
    "laptop": {
      "position": [
        2,
        2
      ],
      "facing": "+x"
    },
    "shelf": {
      "position": [
        4,
        4
      ]
    },
    "yellow door": {
      "position": [
        2,
        6
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidates (A, B, C, D, E, F) are all positioned in separated pockets of space beyond walls/doorway lines that have not been directly observed yet, so they count as unexplored/unobserved regions.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

######################
#......########EC....#
#......*......#A.....#
#......#......#......#
#......#......+F.....#
#......#......#.....B#
#......#......#..D...#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: C, A, E

🧠 Cognitive Map Metrics

Global:

dir: 0.790

facing: 1

pos: 0.736

overall: 0.842

Global (Full):

dir: 0.692

facing: 0.900

pos: 0.687

overall: 0.760

Local:

dir: 0.800

facing: 1

pos: 0.851

overall: 0.884

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.488

is_action_fail: False

step: 18

action_counts:

move: 4

rotate: 12

return: 0

observe: 18

term: 0

forced_term: 0

query: 0

information_gain: 0.885

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 19

👤 Environment Observation (click to toggle)
You jumped to brown door. You observe:
• airplane: front, slightly far, facing right
• laptop: front-right, mid distance, facing right
• shelf: front-right, slightly far
• yellow door: front-slight-right, slightly far, on front wall.
You have a maximum of 2 exploration steps left.
Strictly follow t...

🤔 Assistant Thinking (click to toggle)
With only 2 steps left and the yellow door currently visible, jump to the yellow doorway to regain the other room’s view (truck/suitcase/backpack area) and observe there to try to finally spot the alarmclock....

💬 Assistant Action
Actions: [JumpTo(yellow door), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single globally-referenced integer-coordinate map (origin at initial agent position), including only objects/doors that were observed; infer approximate consistent positions and cardinal facings from the action/rotation history.
FINAL ANSWER:
{
"agent": { "position": [5, -4], "f...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      5,
      -4
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      5,
      -4
    ]
  },
  "suitcase": {
    "position": [
      8,
      -6
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      8,
      -4
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      10,
      -3
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      3,
      -6
    ]
  },
  "bike": {
    "position": [
      1,
      -7
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      1,
      -5
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      1,
      -1
    ]
  },
  "cap": {
    "position": [
      -3,
      -3
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -2,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -4,
      -4
    ]
  },
  "pillow": {
    "position": [
      -2,
      -1
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Construct a local (agent-centric) map from the current FOV at the yellow doorway; choose integer coordinates consistent with the reported bearings/distances and the suitcase–truck proximity.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"truck": {
"position": [0, 3],
"fa...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "truck": {
      "position": [
        0,
        3
      ],
      "facing": "+y"
    },
    "suitcase": {
      "position": [
        1,
        3
      ],
      "facing": "-x"
    },
    "backpack": {
      "position": [
        -1,
        5
      ],
      "facing": "-x"
    }
  }
}

🎯 Ground Truth

{
  "suitcase": {
    "position": [
      2,
      3
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      0,
      3
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      -2,
      5
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "truck": {
      "position": [
        0,
        3
      ],
      "facing": "+y"
    },
    "suitcase": {
      "position": [
        1,
        3
      ],
      "facing": "-x"
    },
    "backpack": {
      "position": [
        -1,
        5
      ],
      "facing": "-x"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A-F are all placed in areas separated by walls/partitions from the currently observed space (especially the upper-right and lower-right pockets), indicating unexplored regions.
FINAL ANSWER:
{"unexplored":["A","B","C","D","E","F"]}...

🗺️ Symbolic Fog Map

######################
#......########CD....#
#......+......#B..E..#
#......#......#..A...#
#......#......*......#
#......#......#......#
#......#......#..F...#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: D, C, B

🧠 Cognitive Map Metrics

Global:

dir: 0.648

facing: 1

pos: 0.588

overall: 0.745

Global (Full):

dir: 0.567

facing: 0.900

pos: 0.548

overall: 0.672

Local:

dir: 0.833

facing: 1

pos: 0.889

overall: 0.907

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.488

is_action_fail: False

step: 19

action_counts:

move: 5

rotate: 12

return: 0

observe: 19

term: 0

forced_term: 0

query: 0

information_gain: 0.885

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 20

👤 Environment Observation (click to toggle)
You jumped to yellow door. You observe:
• suitcase: front-right, mid distance, facing left
• truck: front, mid distance, facing forward
• backpack: front-slight-left, slightly far, facing left
Assume your current facing direction is called "north":
• suitcase is east, near to truck.
You have a maxi...

🤔 Assistant Thinking (click to toggle)
Alarmclock is still unobserved; with one step left, the best chance is to rotate 90° from the yellow doorway to scan a new 90° sector and observe for the alarmclock....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Infer a single consistent global coordinate assignment from all egocentric observations and proximity constraints; include only observed entities with integer positions and cardinal facings where available.
FINAL ANSWER:
{
"agent": { "position": [4, -3], "facing": "south" },
"yellow door"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      4,
      -3
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      4,
      -3
    ]
  },
  "brown door": {
    "position": [
      -1,
      0
    ]
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      7,
      -3
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -5
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      8,
      -1
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      2,
      -6
    ]
  },
  "laptop": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      -1,
      -6
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      -4,
      0
    ]
  },
  "cap": {
    "position": [
      -6,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -6,
      -4
    ]
  },
  "white chair": {
    "position": [
      -4,
      -4
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map from the current FOV; only the shelf is visible, placed at an integer coordinate consistent with front-right and mid distance.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"shelf": {
"position": [2, 2]
}
}
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shelf": {
      "position": [
        2,
        2
      ]
    }
  }
}

🎯 Ground Truth

{
  "shelf": {
    "position": [
      1,
      2
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shelf": {
      "position": [
        2,
        2
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The only object never observed is likely in one of the map pockets we never got visibility into; all candidate points (A, B, C, D, E, F) lie in such unobserved regions relative to explored viewpoints.
FINAL ANSWER:
{"unexplored":["A","B","C","D","E","F"]}...

🗺️ Symbolic Fog Map

######################
#......########CFE...#
#......+......#D.....#
#......#......#......#
#......#......*......#
#......#......#......#
#......#......#..AB..#
########......########
######################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: C, F, D

🧠 Cognitive Map Metrics

Global:

dir: 0.771

facing: 1

pos: 0.749

overall: 0.840

Global (Full):

dir: 0.675

facing: 0.900

pos: 0.699

overall: 0.758

Local:

dir: 1

facing: 1

pos: 0.866

overall: 0.955

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.488

is_action_fail: False

step: 20

action_counts:

move: 5

rotate: 13

return: 0

observe: 20

term: 0

forced_term: 0

query: 0

information_gain: 0.885

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 21

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• shelf: front-right, mid distance.
You have a maximum of 0 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
No exploration steps remain; terminate....

💬 Assistant Action
Actions: [Term()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will output a single consistent global map (integer coordinates) for all observed objects/doors relative to the initial origin, including known facing for objects where it was reported; unobserved objects (alarmclock) are omitted.
FINAL ANSWER: {
"agent": { "position": [4, -4], "facing": ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      4,
      -4
    ]
  },
  "brown door": {
    "position": [
      0,
      -2
    ]
  },
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -6
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      9,
      -2
    ],
    "facing": "north"
  },
  "shelf": {
    "position": [
      2,
      -6
    ]
  },
  "laptop": {
    "position": [
      -1,
      -4
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      -1,
      -6
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      -3,
      -2
    ]
  },
  "cap": {
    "position": [
      -5,
      -4
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      -5,
      -5
    ]
  },
  "white chair": {
    "position": [
      -5,
      -6
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Observed)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "airplane": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      -9,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "white chair": {
    "position": [
      -7,
      -4
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      7,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      -2
    ],
    "facing": "south"
  },
  "shelf": {
    "position": [
      3,
      -4
    ],
    "facing": "east"
  },
  "wine": {
    "position": [
      -9,
      -4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      7,
      -2
    ],
    "facing": "east"
  },
  "pillow": {
    "position": [
      -7,
      0
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      9,
      0
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      4,
      -2
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      4,
      -2
    ],
    "facing": "south"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.762

facing: 1

pos: 0.666

overall: 0.809

Global (Full):

dir: 0.667

facing: 0.900

pos: 0.622

overall: 0.729

Local:

(none)

Local (Newly):

(none)

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.488

is_action_fail: False

step: 21

action_counts:

move: 5

rotate: 13

return: 0

observe: 20

term: 0

forced_term: 1

query: 0

information_gain: 0.885

reward: -0.100

is_done: True

is_valid_action: True

🔄 FB Turn 22

👤 Environment Observation (click to toggle)
You have returned to the initial position and face north.
There are 4 objects in the room that have been changed (position or orientation).
Note one object is either moved or rotated, not both.
Goal: Explore the room again and identify which objects have been changed and how with minimum costs.
Use ...

🤔 Assistant Thinking (click to toggle)
Rebuild key reference observations from the same viewpoints as before to spot differences. Start with an Observe() from the initial pose (origin, facing north) to check if anything newly appears in the initial FOV....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must provide a complete global JSON cognitive map (agent, all 12 objects, and 2 doors) using the initial position as origin, with integer coordinates and cardinal facings where known; for unobserved/unknown facings, omit the facing key.
FINAL ANSWER:
```json
{
"agent": { "position": [0, 0...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.894

facing: 1

pos: 0.891

overall: 0.928

🧾 False Belief Object Sets

all_objects: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

all_changed_objects: ['alarmclock', 'laptop', 'suitcase', 'truck']

newly_observed_changed (this turn): []

unchanged_objects: ['airplane', 'backpack', 'bike', 'brown door', 'cap', 'pillow', 'shelf', 'white chair', 'wine', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

🧭 False Belief Info

step: 1

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'truck', 'ori': True}, {'name': 'suitcase', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'laptop', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 23

👤 Environment Observation (click to toggle)
You observe: No objects in field of view..
You have a maximum of 14 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
Match the earlier scan: rotate to face east (clockwise 90°) and observe to compare airplane/yellow door presence, positions, and orientations versus the prior map....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a complete global cognitive map (all objects + both doors + agent) using a consistent integer-coordinate layout anchored at the initial agent origin.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "east" },
"airplane": { "position": [3, 0], "facing": "south" },
"yell...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.121

facing: 0.500

pos: 0.109

overall: 0.243

🧾 False Belief Object Sets

all_objects: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

all_changed_objects: ['alarmclock', 'laptop', 'suitcase', 'truck']

newly_observed_changed (this turn): []

unchanged_objects: ['airplane', 'backpack', 'bike', 'brown door', 'cap', 'pillow', 'shelf', 'white chair', 'wine', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

🧭 False Belief Info

step: 2

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'truck', 'ori': True}, {'name': 'suitcase', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'laptop', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: ['airplane', 'yellow door']

🔄 FB Turn 24

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• airplane: front, mid distance, facing right
• yellow door: front-right, slightly far, on front wall.
You have a maximum of 13 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
Jump to the yellow doorway and observe from there, since it previously revealed suitcase/truck/backpack; compare their relative bearings/distances/orientations to the earlier baseline to detect changes....

💬 Assistant Action
Actions: [JumpTo(yellow door), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a consistent global 2D integer-coordinate map anchored at initial agent origin [0,0] with north as +y, placing objects/doors based on observed relative layout; include all objects and current agent pose (at yellow door, facing east).
FINAL ANSWER:
```json
{
"agent": { "position":...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: truck:

dir: None

pos: None

facing: 0

overall: None

Retention: truck:

dir: None

pos: None

facing: 0

overall: None

Changed: suitcase:

dir: None

pos: None

facing: 0

overall: None

Retention: suitcase:

dir: None

pos: None

facing: 0

overall: None

Unchanged (all):

dir: 0.121

facing: 0.167

pos: 0.125

overall: 0.138

🧾 False Belief Object Sets

all_objects: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

all_changed_objects: ['alarmclock', 'laptop', 'suitcase', 'truck']

newly_observed_changed (this turn): ['suitcase', 'truck']

unchanged_objects: ['airplane', 'backpack', 'bike', 'brown door', 'cap', 'pillow', 'shelf', 'white chair', 'wine', 'yellow door']

pred_changed_keys: ['suitcase', 'truck']

pred_unchanged_keys: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

🧭 False Belief Info

step: 3

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'truck', 'ori': True}, {'name': 'suitcase', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'laptop', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['truck', 'suitcase']

newly_observed_unchanged_objects: ['backpack']

🔄 FB Turn 25

👤 Environment Observation (click to toggle)
You jumped to yellow door. You observe:
• suitcase: front-right, mid distance, facing right
• truck: front, mid distance, facing backward
• backpack: front-slight-left, slightly far, facing left
Assume your current facing direction is called "north":
• suitcase is east, near to truck.
You have a ma...

🤔 Assistant Thinking (click to toggle)
We already see two clear orientation differences (suitcase now facing right vs left; truck now facing backward vs forward). Next, replicate the earlier 90° sector sweeps from the yellow doorway to check shelf/bike/laptop/brown door for additional changes....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must output a complete JSON cognitive map (agent, all objects, both doors) in the global frame (initial position as [0,0], global cardinal facings). With no Query data available, I will provide the best-fit integer-coordinate map consistent with the observed relative bearings/distances and ...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.591

facing: 1

pos: 0.714

overall: 0.768

🧾 False Belief Object Sets

all_objects: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

all_changed_objects: ['alarmclock', 'laptop', 'suitcase', 'truck']

newly_observed_changed (this turn): []

unchanged_objects: ['airplane', 'backpack', 'bike', 'brown door', 'cap', 'pillow', 'shelf', 'white chair', 'wine', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

🧭 False Belief Info

step: 4

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'truck', 'ori': True}, {'name': 'suitcase', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'laptop', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: ['shelf']

🔄 FB Turn 26

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• shelf: front-right, mid distance.
You have a maximum of 11 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
Continue the same sweep from the yellow doorway: rotate another 90° to bring bike/laptop/brown door into view and compare to the baseline....

💬 Assistant Action
Actions: [Rotate(90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will provide a complete global JSON cognitive map (agent, all objects, both doors) in the required schema with integer coordinates and cardinal facings where applicable.
FINAL ANSWER:
{
"agent": { "position": [5, -3], "facing": "west" },
"airplane": { "position": [3, 0], "facing": "sout...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.121

facing: 0.500

pos: 0.097

overall: 0.239

🧾 False Belief Object Sets

all_objects: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

all_changed_objects: ['alarmclock', 'laptop', 'suitcase', 'truck']

newly_observed_changed (this turn): []

unchanged_objects: ['airplane', 'backpack', 'bike', 'brown door', 'cap', 'pillow', 'shelf', 'white chair', 'wine', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

🧭 False Belief Info

step: 5

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'truck', 'ori': True}, {'name': 'suitcase', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'laptop', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: ['brown door', 'bike']

🔄 FB Turn 27

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• bike: front-slight-left, slightly far, facing right
• brown door: front-slight-right, slightly far, on front wall.
You have a maximum of 10 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Action...

🤔 Assistant Thinking (click to toggle)
Laptop is now not visible from the same yellow-door-facing direction where it previously was, indicating a position change. To find the 4th changed object, jump to the brown doorway and observe the cap/white chair/wine/pillow set to compare against baseline....

💬 Assistant Action
Actions: [JumpTo(brown door), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single globally-referenced integer-coordinate map (origin at initial agent position) consistent with observed bearings/distances; include agent, all 12 objects, and both doors, adding facing only where observed.
FINAL ANSWER:
{
"agent": { "position": [2, -3], "facing": "west" },...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: laptop:

dir: None

pos: 0.634

facing: None

overall: None

Retention: laptop:

dir: None

pos: 0.486

facing: None

overall: None

Changed: alarmclock:

dir: None

pos: 0.815

facing: None

overall: None

Retention: alarmclock:

dir: None

pos: None

facing: None

overall: None

Unchanged (all):

dir: 0.591

facing: 0.833

pos: 0.504

overall: 0.643

🧾 False Belief Object Sets

all_objects: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

all_changed_objects: ['alarmclock', 'laptop', 'suitcase', 'truck']

newly_observed_changed (this turn): ['alarmclock', 'laptop']

unchanged_objects: ['airplane', 'backpack', 'bike', 'brown door', 'cap', 'pillow', 'shelf', 'white chair', 'wine', 'yellow door']

pred_changed_keys: ['alarmclock', 'laptop']

pred_unchanged_keys: ['airplane', 'alarmclock', 'backpack', 'bike', 'brown door', 'cap', 'laptop', 'pillow', 'shelf', 'suitcase', 'truck', 'white chair', 'wine', 'yellow door']

🧭 False Belief Info

step: 6

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'truck', 'ori': True}, {'name': 'suitcase', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'laptop', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['laptop', 'alarmclock']

newly_observed_unchanged_objects: ['cap', 'white chair', 'pillow', 'wine']

🔄 FB Turn 28

👤 Environment Observation (click to toggle)
You jumped to brown door. You observe:
• cap: front-slight-left, slightly far, facing right
• alarmclock: front-left, slightly far, facing right
• white chair: front-left, slightly far, facing backward
• laptop: front-slight-right, slightly far, facing left
• wine: front-left, slightly far
• pillow...

🤔 Assistant Thinking (click to toggle)
Compared to the baseline observations from the same viewpoints: suitcase and truck show different facing directions at the yellow doorway (orientation changes). Laptop is no longer visible from the yellow doorway where it previously was (position change). Alarmclock is now visible from the brown doo...

💬 Assistant Action
Actions: [Term(changes="suitcase: orientation, truck: orientation, laptop: position, alarmclock: position")]

📈 Turn Metrics

reward: 1

is_done: True

is_valid_action: True

success: 1

🧭 False Belief Info

step: 7

correctly_identified_changes: 1

f1_overall: 1

f1_position: 1

f1_facing: 1

action_cost: 6

action_cost_after_seen: 0

ground_truth_changes: [{'name': 'truck', 'ori': True}, {'name': 'suitcase', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'laptop', 'pos': True}]

reported_changes: [{'name': 'suitcase', 'ori': True}, {'name': 'truck', 'ori': True}, {'name': 'laptop', 'pos': True}, {'name': 'alarmclock', 'pos': True}]

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

📊 Task: DirectionEvaluationTask

Question 1 (ID: 3af22411dc8e4653)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where white chair is relative to laptop.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From the brown door viewpoint (facing global south), the white chair was front-right (southwest) while the laptop was front-left (southeast), so the chair is west of the laptop. Their reported distance-from-viewpoint bins suggest their separation is best matched by slightly far.
FINAL ANSWER:...

✅ Evaluation Results

id: 3af22411dc8e4653

question: You return to your starting position and face north. From a Top-Down map, describe where white chair is relative to laptop. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: west, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Evaluation state — Q1: DirectionEvaluationTask

Question 2 (ID: f2b0e3268c58b4c8)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where white chair is relative to pillow.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From the pillow position (global reference restored), white chair was observed directly in front while facing south, at mid distance; therefore it is south of pillow by a mid distance.
FINAL ANSWER:
south, mid distance...

✅ Evaluation Results

id: f2b0e3268c58b4c8

question: You return to your starting position and face north. From a Top-Down map, describe where white chair is relative to pillow. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: south, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: ae731068ff074970)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where airplane is relative to pillow.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Estimate positions from observations: airplane is several meters to the east of the pillow, at a distance in the 4–8m bin.
FINAL ANSWER:
east, slightly far...

✅ Evaluation Results

id: ae731068ff074970

question: You return to your starting position and face north. From a Top-Down map, describe where airplane is relative to pillow. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

📊 Task: PovEvaluationTask

Question 1 (ID: 77dc57ad53ccff4c)

❓ Evaluation Question (click to toggle)
Now you jump to airplane's direction, facing its direction.
Describe where bike is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
After jumping to the airplane and facing the airplane’s facing direction (south), the bike would be ahead and to the left.
FINAL ANSWER:
front-left, slightly far...

✅ Evaluation Results

id: 77dc57ad53ccff4c

question: Now you jump to airplane's direction, facing its direction. Describe where bike is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front-right, slightly far

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 2 (ID: ba63e01f9f8cf08f)

❓ Evaluation Question (click to toggle)
Now you jump to truck's direction, facing its direction.
Describe where backpack is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From the earlier view at the yellow door, backpack was farther and slightly left of the truck, so from the truck it should be in front and slightly left at a mid distance.
FINAL ANSWER:
front-slight-left, mid distance...

✅ Evaluation Results

id: ba63e01f9f8cf08f

question: Now you jump to truck's direction, facing its direction. Describe where backpack is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 3 (ID: fe59923b2b52093d)

❓ Evaluation Question (click to toggle)
Now you jump to airplane's direction, facing its direction.
Describe where shelf is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From the prior relative placement, the shelf is slightly to the right of where the airplane is facing and a few meters away.
FINAL ANSWER:
front-slight-right, mid distance...

✅ Evaluation Results

id: fe59923b2b52093d

question: Now you jump to airplane's direction, facing its direction. Describe where shelf is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: db00fbb4ebb9f189)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that bike is front-right, slightly far, facing backward; shelf is front, mid distance; yellow door is front-left, mid distance, on left wall.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
From this viewpoint, the yellow door appears on the left wall and objects like the shelf and bike are seen ahead through/near that doorway, matching a position inside the adjoining room where the truck was located.
FINAL ANSWER:
truck...

✅ Evaluation Results

id: db00fbb4ebb9f189

question: Now you jump to an object's position, facing its direction. You observe that bike is front-right, slightly far, facing backward; shelf is front, mid distance; yellow door is front-left, mid distance, on left wall. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: airplane

final_pos: [13, 6]

final_ori: [0, -1]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'shelf', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'yellow door', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'on left wall'}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 1

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: default

Question 2 (ID: dbd8ca2192fa7515)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that pillow is front-right, mid distance.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
Pillow being front-right when I’m aligned to the object’s facing direction is most consistent with standing at the cap (facing north), with the pillow located to its northeast at a mid distance.
FINAL ANSWER:
cap...

✅ Evaluation Results

id: dbd8ca2192fa7515

question: Now you jump to an object's position, facing its direction. You observe that pillow is front-right, mid distance. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: cap

final_pos: [1, 4]

final_ori: [0, 1]

final_observation: [{'name': 'pillow', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': None}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 2

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

Question 3 (ID: 2c823d1f2d940432)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that brown door is front-left, slightly far, on front wall.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
From the yellow doorway layout, the brown door is positioned northwest of the truck. If I’m standing at the truck and facing its “forward” direction, the brown door would appear front-left at a moderate (“slightly far”) distance.
FINAL ANSWER:
truck...

✅ Evaluation Results

id: 2c823d1f2d940432

question: Now you jump to an object's position, facing its direction. You observe that brown door is front-left, slightly far, on front wall. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: white chair

final_pos: [3, 2]

final_ori: [1, 0]

final_observation: [{'name': 'brown door', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 2

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: default

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 6d169a70c94f49de)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-180)
2. Jump to the object at front-left, slightly far.
3. Rotate(90)
4. Among objects which are front-right, slightly far to you, you jump to the 2nd from left also farthest one.
5. Rotat...

🤔 Assistant Answer (click to toggle)
THINK:
From start, Rotate(-180) makes the yellow door the front-left slightly-far target, so I jump to it; after Rotate(90) and selecting among front-right slightly-far items, the jump lands at the shelf; after Rotate(-180) I face east, placing the yellow door outside the ±45° FOV to my left-front, ...

✅ Evaluation Results

id: 6d169a70c94f49de

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-180) 2. Jump to the object at front-left, slightly far. 3. Rotate(90) 4. Among objects which are front-right, slightly far to you, you jump to the 2nd from left also farthest one. 5. Rotate(-180) After executing the actions, what is the ego relation of yellow door relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-slight-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Question 2 (ID: 6a9b2df21e9fa7d6)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-90)
2. Jump to the object at front, mid distance.
3. Among objects which are front-left, slightly far to you, you jump to the 2nd from left also farthest one.
4. Rotate(-180)

After execut...

🤔 Assistant Answer (click to toggle)
THINK:
After returning to start facing north: Rotate(-90) makes me face west. The only front, mid-distance target consistent with earlier observations from the brown doorway is the brown door, so Jump lands me at the brown door still facing west. From there, among the front-left, slightly-far object...

✅ Evaluation Results

id: 6a9b2df21e9fa7d6

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front, mid distance. 3. Among objects which are front-left, slightly far to you, you jump to the 2nd from left also farthest one. 4. Rotate(-180) After executing the actions, what is the ego relation of white chair relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: b48679bb04763dd4)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-180)
2. Jump to the object at front-right, mid distance.
3. Rotate(-90)
4. Jump to the object at front, slightly far.

After executing the actions, what is the ego relation of suitcase rel...

🤔 Assistant Answer (click to toggle)
THINK:
After the given rotations/jumps, the final position corresponds to being at the yellow door facing east, where the suitcase was observed as front-right at a mid distance.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: b48679bb04763dd4

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-180) 2. Jump to the object at front-right, mid distance. 3. Rotate(-90) 4. Jump to the object at front, slightly far. After executing the actions, what is the ego relation of suitcase relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: ba3e02e16a5c87a1)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
suitcase is at front-left, slightly far, facing backward; truck is at front-left, mid distance, facing left; yellow door is at...

🤔 Assistant Answer (click to toggle)
THINK:
Need a valid move-only sequence starting with a rotation (no JumpTo first), using previously observable waypoints to end at a pose where yellow door is on the right wall and suitcase/truck are in front-left. Use yellow door as transit, then jump to shelf and rotate to final facing.
FINAL ANSW...

✅ Evaluation Results

id: ba3e02e16a5c87a1

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: suitcase is at front-left, slightly far, facing backward; truck is at front-left, mid distance, facing left; yellow door is at front-right, mid distance, on right wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [15, 6]

final_ori: [0, -1]

room_id: 3

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'yellow door'], ['rotate', -90], ['jumpto', 'alarmclock'], ['rotate', 180]]

final_observation: [{'name': 'suitcase', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}, {'name': 'yellow door', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'on right wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: False

final_pos: [13.0, 2.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: default

Question 2 (ID: acb07825c6116973)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
airplane is at front-left, mid distance, facing backward; alarmclock is at front-right, mid distance, facing forward

What act...

🤔 Assistant Answer (click to toggle)
THINK:
From the start, rotate to bring the yellow door into view, jump to it, rotate to bring the laptop into view, jump to the laptop, then rotate to the final facing direction.
FINAL ANSWER:
Rotate(90), JumpTo(yellow door), Rotate(180), JumpTo(laptop), Rotate(180)...

✅ Evaluation Results

id: acb07825c6116973

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: airplane is at front-left, mid distance, facing backward; alarmclock is at front-right, mid distance, facing forward What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [14, 4]

final_ori: [0, 1]

room_id: [1, 3]

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'initial_pos'], ['rotate', -90]]

final_observation: [{'name': 'airplane', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'alarmclock', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: False

final_pos: [9.0, 4.0]

final_ori: [1, 0]

visible_match: False

score: 0

evaluation_mode: default

Question 3 (ID: 3f3f07a01338a14c)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
alarmclock is at front, mid distance, facing right; truck is at front-left, mid distance, facing backward; yellow door is at f...

🤔 Assistant Answer (click to toggle)
THINK:
To see the yellow door plus nearby truck and the (now visible) alarmclock, move into room 2 via the yellow door, go to the suitcase area (known to be near the truck), then turn to face back toward the yellow door.
FINAL ANSWER:
Rotate(90), JumpTo(yellow door), JumpTo(suitcase), Rotate(180)...

✅ Evaluation Results

id: 3f3f07a01338a14c

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: alarmclock is at front, mid distance, facing right; truck is at front-left, mid distance, facing backward; yellow door is at front-slight-left, slightly far, on front wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [19, 6]

final_ori: [-1, 0]

room_id: 3

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'yellow door'], ['jumpto', 'backpack'], ['rotate', 180]]

final_observation: [{'name': 'alarmclock', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'yellow door', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [17.0, 2.0]

final_ori: [-1, 0]

visible_match: False

score: 0

evaluation_mode: default

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 9c329788def30f4f)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: white chair, laptop, pillow, airplane.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
I did not obtain any exact allocentric coordinates (no Query actions were used), and the observations only provided coarse, binned bearings/distances. That information is insufficient to uniquely determine integer (x, y) coordinates for the requested objects relative to the origin.
FINAL ANSW...

✅ Evaluation Results

id: 9c329788def30f4f

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: white chair, laptop, pillow, airplane. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[-7, -4], [-1, -2], [-7, 0], [3, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

error: invalid_prediction_format

score: 0

evaluation_mode: default

Question 2 (ID: 9bdf383982e77bb0)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: white chair, pillow, laptop, bike.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Infer a consistent integer-coordinate layout from the observed relative bearings/distances and the known jump positions (notably from the brown door), then report the requested objects’ allocentric coordinates relative to the start.
FINAL ANSWER:
(-5, -6); (-3, -1); (2, -3); (2, -4)...

✅ Evaluation Results

id: 9bdf383982e77bb0

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: white chair, pillow, laptop, bike. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[-7, -4], [-7, 0], [-1, -2], [-1, -4]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.633

threshold: 0.900

score: 0.633

evaluation_mode: default

Question 3 (ID: 9ff68c1e426c4bd7)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: airplane, pillow, suitcase, white chair, backpack.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Infer a consistent integer-coordinate layout matching all observed bearing/distance bins and door-relative viewpoints; output requested objects in order.
FINAL ANSWER:
(3, 0); (-5, 0); (7, -4); (-5, -3); (9, -1)...

✅ Evaluation Results

id: 9ff68c1e426c4bd7

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: airplane, pillow, suitcase, white chair, backpack. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[3, 0], [-7, 0], [7, -4], [-7, -4], [9, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.912

threshold: 0.900

score: 0.912

evaluation_mode: default

📊 Task: RotEvaluationTask

Question 1 (ID: 9ce88e9c1df16a31)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: laptop, shelf, wine.
List them in th...

🤔 Assistant Answer (click to toggle)
THINK:
Order is determined by each object’s absolute bearing from the start (0,0) as I rotate counterclockwise from facing north; wine lies to the west/southwest side, while shelf is more southerly/easterly than laptop, so it comes before laptop.
FINAL ANSWER:
wine, shelf, laptop...

✅ Evaluation Results

id: 9ce88e9c1df16a31

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, shelf, wine. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['wine', 'laptop', 'shelf']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Question 2 (ID: 36b3f8b4d004b955)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: laptop, shelf, white chair.
List the...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s bearing from the start (0,0) facing north, convert to CCW-rotation encounter time t = (-bearing) mod 360, sort by increasing t; break ties by nearer-first.
FINAL ANSWER:
white chair, laptop, shelf...

✅ Evaluation Results

id: 36b3f8b4d004b955

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, shelf, white chair. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['white chair', 'laptop', 'shelf']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 9eefbbaaede91b15)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: airplane, laptop, pillow, shelf.
Lis...

🤔 Assistant Answer (click to toggle)
THINK:
Determine each object’s bearing from the start (0,0) and order them by when the agent’s forward direction matches those bearings during a counterclockwise sweep (north → west → south → east → north). Airplane lies due east; pillow lies due west; shelf and laptop lie in the southeast, with she...

✅ Evaluation Results

id: 9eefbbaaede91b15

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: airplane, laptop, pillow, shelf. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['pillow', 'laptop', 'shelf', 'airplane']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

📊 Task: RotDualEvaluationTask

Question 1 (ID: 4dca2fe8cca65e49)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You performed a complete 360° rotation in place.
Assume all walls are removed (you can see through walls), so every object is visible.
During the rotation, these objects appeared directly in front of you in this order:
wine, laptop, shelf

Based o...

🤔 Assistant Answer (click to toggle)
THINK:
During a full rotation starting facing north, the order of objects appearing straight ahead matches decreasing bearings (highest to lower), which corresponds to a counterclockwise rotation: wine → laptop → shelf.
FINAL ANSWER:
counterclockwise...

✅ Evaluation Results

id: 4dca2fe8cca65e49

question: You return to your starting position and face north. You performed a complete 360° rotation in place. Assume all walls are removed (you can see through walls), so every object is visible. During the rotation, these objects appeared directly in front of you in this order: wine, laptop, shelf Based on this sequence, in which direction did you rotate? Answer format: clockwise or counterclockwise Example: clockwise

action: None

answer: counterclockwise

task_type: RotDualEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: 8f5d22c4372c7657)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You performed a complete 360° rotation in place.
Assume all walls are removed (you can see through walls), so every object is visible.
During the rotation, these objects appeared directly in front of you in this order:
white chair, laptop, shelf

...

🤔 Assistant Answer (click to toggle)
THINK:
As the facing direction sweeps, objects appear in the order they are encountered along the rotation direction. The sequence white chair → laptop → shelf matches a sweep from southwest toward southeast, i.e., counterclockwise.
FINAL ANSWER:
counterclockwise...

✅ Evaluation Results

id: 8f5d22c4372c7657

question: You return to your starting position and face north. You performed a complete 360° rotation in place. Assume all walls are removed (you can see through walls), so every object is visible. During the rotation, these objects appeared directly in front of you in this order: white chair, laptop, shelf Based on this sequence, in which direction did you rotate? Answer format: clockwise or counterclockwise Example: clockwise

action: None

answer: counterclockwise

task_type: RotDualEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 3594327599c02775)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You performed a complete 360° rotation in place.
Assume all walls are removed (you can see through walls), so every object is visible.
During the rotation, these objects appeared directly in front of you in this order:
pillow, laptop, shelf, airpl...

🤔 Assistant Answer (click to toggle)
THINK:
Airplane was seen directly ahead when facing east (after a clockwise 90° turn earlier), so it lies roughly east of the start. In a full 360° rotation starting from north, an east-aligned object would appear near the end only if rotating counterclockwise.
FINAL ANSWER:
counterclockwise...

✅ Evaluation Results

id: 3594327599c02775

question: You return to your starting position and face north. You performed a complete 360° rotation in place. Assume all walls are removed (you can see through walls), so every object is visible. During the rotation, these objects appeared directly in front of you in this order: pillow, laptop, shelf, airplane Based on this sequence, in which direction did you rotate? Answer format: clockwise or counterclockwise Example: clockwise

action: None

answer: counterclockwise

task_type: RotDualEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: dfb33925c33ad150)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (0, -3) and face north.
What is the egocentric relation of airplane?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Assume airplane was ~3m east and ~0m north of the origin from earlier observations; from (0,-3) facing north this places it diagonally ahead-right at a bit over 4m.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: dfb33925c33ad150

question: Still treat your initial position as origin (0, 0) You move to (0, -3) and face north. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: a9cd391496e7f4bd)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (-2, -2) and face east.
What is the egocentric relation of airplane?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From (-2, -2) facing east, the airplane is ahead and a little to the left at a moderate-far range.
FINAL ANSWER:
front-slight-left, slightly far...

✅ Evaluation Results

id: a9cd391496e7f4bd

question: Still treat your initial position as origin (0, 0) You move to (-2, -2) and face east. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-slight-left, slightly far

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 4bb34213337def71)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (1, 0) and face south.
What is the egocentric relation of bike?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From the final mapped position, the bike lies straight ahead of the agent when facing south, at a distance of 4 units, which bins to mid distance.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: 4bb34213337def71

question: Still treat your initial position as origin (0, 0) You move to (1, 0) and face south. What is the egocentric relation of bike? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 589844ce1a3db594)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: airplane is at front-right, slightly far, facing backward; laptop is at front-left, near, facing backward; brown door is at front-left, slightly far, on left wall
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate...

🤔 Assistant Answer (click to toggle)
THINK:
Infer integer coordinates consistent with all prior bearings/distance bins: set airplane at (3,0), brown door at (-3,0), laptop at (-1,-2). Solve for the new agent position P such that laptop is front-left near, airplane front-right slightly far, brown door front-left slightly far; this yield...

✅ Evaluation Results

id: 589844ce1a3db594

question: You move to a new location and face north. You observe: airplane is at front-right, slightly far, facing backward; laptop is at front-left, near, facing backward; brown door is at front-left, slightly far, on left wall Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [0, -3]

final_pos: [10, 3]

final_ori: [0, 1]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'airplane', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'laptop', 'direction': 'front-left', 'distance': 'near', 'orientation': 'facing backward'}, {'name': 'brown door', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'on left wall'}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: default

Question 2 (ID: ccba734e7d8b0db2)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: airplane is at front-slight-left, slightly far, facing right; laptop is at front, near, facing right; shelf is at front-slight-right, slightly far
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?

Answer ...

🤔 Assistant Answer (click to toggle)
THINK:
Given I’m facing east and the laptop is directly in front at near distance, my new position is one grid step west of the laptop along the same y. The relative layout with airplane front-slight-left and shelf front-slight-right at slightly-far distance is consistent with the new position being...

✅ Evaluation Results

id: ccba734e7d8b0db2

question: You move to a new location and face east. You observe: airplane is at front-slight-left, slightly far, facing right; laptop is at front, near, facing right; shelf is at front-slight-right, slightly far Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-2, -2]

final_pos: [8, 4]

final_ori: [1, 0]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'airplane', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'laptop', 'direction': 'front', 'distance': 'near', 'orientation': 'facing right'}, {'name': 'shelf', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.702

raw_score: 0.702

score: 0.702

evaluation_mode: default

Question 3 (ID: 3663a671d8f461de)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: bike is at front-right, slightly far, facing backward; laptop is at front-right, mid distance, facing forward; shelf is at front-left, slightly far
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?

Answe...

🤔 Assistant Answer (click to toggle)
THINK:
Use the observed relative bearings/distances to bike, laptop, and shelf (whose coordinates can be inferred consistently from earlier observations) to solve for the unique agent position that makes laptop front-right mid, bike front-right slightly far, and shelf front-left slightly far while f...

✅ Evaluation Results

id: 3663a671d8f461de

question: You move to a new location and face south. You observe: bike is at front-right, slightly far, facing backward; laptop is at front-right, mid distance, facing forward; shelf is at front-left, slightly far Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [1, 0]

final_pos: [11, 6]

final_ori: [0, -1]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'laptop', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'shelf', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': None}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: default

📊 Task: DirectionEvaluationTask

Question 1 (ID: 3af22411dc8e4653)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where white chair is relative to laptop.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
I need to output a global cognitive map (integer coordinates) using the initial agent position as origin. No Query() calls were made, so exact coordinates weren’t provided; I’ll construct a single consistent integer layout that satisfies the observed relative bearings/distances/proximities (e...

✅ Evaluation Results

id: 3af22411dc8e4653

question: You return to your starting position and face north. From a Top-Down map, describe where white chair is relative to laptop. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: west, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: f2b0e3268c58b4c8)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where white chair is relative to pillow.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Compile a consistent global map (integer coordinates) from the observed relative bearings/distances at each viewpoint, anchored to the start at (0,0) facing north. Then compute the top-down relative direction and binned distance from pillow to white chair using those coordinates.
FINAL ANSWER...

✅ Evaluation Results

id: f2b0e3268c58b4c8

question: You return to your starting position and face north. From a Top-Down map, describe where white chair is relative to pillow. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: south, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 3 (ID: ae731068ff074970)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where airplane is relative to pillow.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Assign integer coordinates consistent with the observed relative placements (using the start as [0,0], north as +y). Then compute airplane’s direction and binned distance from pillow using those coordinates.
FINAL ANSWER:
<cogmap>
{
"agent": { "position": [4, 4], "facing": "south" },
"air...

✅ Evaluation Results

id: ae731068ff074970

question: You return to your starting position and face north. From a Top-Down map, describe where airplane is relative to pillow. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: 77dc57ad53ccff4c)

❓ Evaluation Question (click to toggle)
Now you jump to airplane's direction, facing its direction.
Describe where bike is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
I will construct a single global grid map (origin at initial agent position) using consistent integer coordinates that satisfy the observed relative bearings/distances and the noted proximity relations. Then, for the question, I place the agent at the airplane’s position and set the agent’s f...

✅ Evaluation Results

id: 77dc57ad53ccff4c

question: Now you jump to airplane's direction, facing its direction. Describe where bike is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front-right, slightly far

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: ba63e01f9f8cf08f)

❓ Evaluation Question (click to toggle)
Now you jump to truck's direction, facing its direction.
Describe where backpack is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
I must output a global cognitive map (integer coordinates) in the initial global frame (origin at start, north = +y), including only objects/doors that were observed. Exact coordinates were never queried, so I’ll provide a single self-consistent integer layout that matches the observed relati...

✅ Evaluation Results

id: ba63e01f9f8cf08f

question: Now you jump to truck's direction, facing its direction. Describe where backpack is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: fe59923b2b52093d)

❓ Evaluation Question (click to toggle)
Now you jump to airplane's direction, facing its direction.
Describe where shelf is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
I will build a single global coordinate map (integers) consistent with the qualitative observations made (bearings/distances/near relations). Unobserved objects (e.g., alarmclock) are excluded.
For the question: after jumping to the airplane and facing the airplane’s facing direction (right...

✅ Evaluation Results

id: fe59923b2b52093d

question: Now you jump to airplane's direction, facing its direction. Describe where shelf is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: db00fbb4ebb9f189)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that bike is front-right, slightly far, facing backward; shelf is front, mid distance; yellow door is front-left, mid distance, on left wall.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
I will build an approximate global grid map (integer coordinates) consistent with the sequence of egocentric observations and the few explicit proximity constraints (e.g., bike west near laptop; suitcase east near truck; cap east near wine; white chair south near wine). Since no Query() actio...

✅ Evaluation Results

id: db00fbb4ebb9f189

question: Now you jump to an object's position, facing its direction. You observe that bike is front-right, slightly far, facing backward; shelf is front, mid distance; yellow door is front-left, mid distance, on left wall. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: airplane

final_pos: [13, 6]

final_ori: [0, -1]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'shelf', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'yellow door', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'on left wall'}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 1

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: dbd8ca2192fa7515)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that pillow is front-right, mid distance.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
Construct a consistent approximate global map (integer coordinates) from the observed relative bearings/distances, then test which object’s facing makes the pillow appear front-right at a mid distance; this matches the cap.
FINAL ANSWER:
<cogmap>
```json
{
"agent": { "position": [6, -3], "f...

✅ Evaluation Results

id: dbd8ca2192fa7515

question: Now you jump to an object's position, facing its direction. You observe that pillow is front-right, mid distance. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: cap

final_pos: [1, 4]

final_ori: [0, 1]

final_observation: [{'name': 'pillow', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': None}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 2

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 2c823d1f2d940432)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that brown door is front-left, slightly far, on front wall.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll construct one globally consistent integer-coordinate map using the first observation as the anchor (agent at origin, facing north). I’ll place objects to satisfy the observed egocentric bearings/distances and the given near-relations (suitcase–truck, bike–laptop, cap–wine, white chair–wi...

✅ Evaluation Results

id: 2c823d1f2d940432

question: Now you jump to an object's position, facing its direction. You observe that brown door is front-left, slightly far, on front wall. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: white chair

final_pos: [3, 2]

final_ori: [1, 0]

final_observation: [{'name': 'brown door', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 2

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 6d169a70c94f49de)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-180)
2. Jump to the object at front-left, slightly far.
3. Rotate(90)
4. Among objects which are front-right, slightly far to you, you jump to the 2nd from left also farthest one.
5. Rotat...

🤔 Assistant Answer (click to toggle)
THINK:
We never used Query(), so exact metric coordinates are underdetermined; I’m assigning a single globally consistent integer grid that matches the observed egocentric bearings/dist-bins and the given near-relations as closely as possible. Then I simulate the given action sequence on this map to...

✅ Evaluation Results

id: 6d169a70c94f49de

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-180) 2. Jump to the object at front-left, slightly far. 3. Rotate(90) 4. Among objects which are front-right, slightly far to you, you jump to the 2nd from left also farthest one. 5. Rotate(-180) After executing the actions, what is the ego relation of yellow door relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-slight-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 2 (ID: 6a9b2df21e9fa7d6)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-90)
2. Jump to the object at front, mid distance.
3. Among objects which are front-left, slightly far to you, you jump to the 2nd from left also farthest one.
4. Rotate(-180)

After execut...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll place all *observed* objects/doors on an integer grid by maintaining consistency with the jump points (agent shares coordinates with jumped-to items) and the reported relative bearings/distances (using representative offsets for “mid/slightly far”). Then I simulate the given action seque...

✅ Evaluation Results

id: 6a9b2df21e9fa7d6

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front, mid distance. 3. Among objects which are front-left, slightly far to you, you jump to the 2nd from left also farthest one. 4. Rotate(-180) After executing the actions, what is the ego relation of white chair relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 3 (ID: b48679bb04763dd4)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-180)
2. Jump to the object at front-right, mid distance.
3. Rotate(-90)
4. Jump to the object at front, slightly far.

After executing the actions, what is the ego relation of suitcase rel...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll construct a single global (cardinal) coordinate map using the initial agent pose as origin, including only objects/doors that were observed. For objects with an observed facing, I convert that facing into a global cardinal direction.
For the action-sequence question, I simulate the rot...

✅ Evaluation Results

id: b48679bb04763dd4

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-180) 2. Jump to the object at front-right, mid distance. 3. Rotate(-90) 4. Jump to the object at front, slightly far. After executing the actions, what is the ego relation of suitcase relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: ba3e02e16a5c87a1)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
suitcase is at front-left, slightly far, facing backward; truck is at front-left, mid distance, facing left; yellow door is at...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll build a consistent integer-coordinate global map (origin at the initial start [0,0], +y is north) using the observed relative relations and known agent headings at the time of those observations. For the action-sequence question, I’ll end at a previously observed object position/orientat...

✅ Evaluation Results

id: ba3e02e16a5c87a1

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: suitcase is at front-left, slightly far, facing backward; truck is at front-left, mid distance, facing left; yellow door is at front-right, mid distance, on right wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [15, 6]

final_ori: [0, -1]

room_id: 3

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'yellow door'], ['rotate', -90], ['jumpto', 'alarmclock'], ['rotate', 180]]

final_observation: [{'name': 'suitcase', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}, {'name': 'yellow door', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'on right wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [19.0, 6.0]

final_ori: [0, -1]

visible_match: False

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: acb07825c6116973)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
airplane is at front-left, mid distance, facing backward; alarmclock is at front-right, mid distance, facing forward

What act...

🤔 Assistant Answer (click to toggle)
THINK:
Build a consistent integer-coordinate global map from all objects/doors that were actually observed, anchoring everything to the initial agent origin and using the strongest relative constraints (notably: airplane seen “front” when facing east from origin; door-to-object and near-relations). ...

✅ Evaluation Results

id: acb07825c6116973

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: airplane is at front-left, mid distance, facing backward; alarmclock is at front-right, mid distance, facing forward What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [14, 4]

final_ori: [0, 1]

room_id: [1, 3]

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'initial_pos'], ['rotate', -90]]

final_observation: [{'name': 'airplane', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'alarmclock', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [14.0, 4.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 3f3f07a01338a14c)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
alarmclock is at front, mid distance, facing right; truck is at front-left, mid distance, facing backward; yellow door is at f...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll build a single consistent global grid (integer coordinates) that fits the observed relative bearings/distances and the reported near-relations (suitcase–truck, bike–laptop, cap–wine, chair–wine), including both doors.
For the action-sequence question: make yellow door visible first (Ro...

✅ Evaluation Results

id: 3f3f07a01338a14c

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: alarmclock is at front, mid distance, facing right; truck is at front-left, mid distance, facing backward; yellow door is at front-slight-left, slightly far, on front wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [19, 6]

final_ori: [-1, 0]

room_id: 3

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'yellow door'], ['jumpto', 'backpack'], ['rotate', 180]]

final_observation: [{'name': 'alarmclock', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'yellow door', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [19.0, 6.0]

final_ori: [-1, 0]

visible_match: True

score: 1

evaluation_mode: prompt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 9c329788def30f4f)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: white chair, laptop, pillow, airplane.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
We never used Query(), so exact coordinates aren’t directly given; I’m constructing a single consistent global map by anchoring the first observed positions to the start (0,0) and propagating relative placements using the coarse bearing/distance bins across jumps/rotations. I then read off th...

✅ Evaluation Results

id: 9c329788def30f4f

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: white chair, laptop, pillow, airplane. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[-7, -4], [-1, -2], [-7, 0], [3, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.687

threshold: 0.900

score: 0.687

evaluation_mode: prompt_cogmap

Question 2 (ID: 9bdf383982e77bb0)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: white chair, pillow, laptop, bike.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
I will output a single global (allocentric) grid map using my start as (0,0), +y as north, +x as east. Since I never used Query(), exact coordinates are inferred to be a consistent integer layout matching all observed relative bearings/distances and the “near” relations. Then I’ll report the ...

✅ Evaluation Results

id: 9bdf383982e77bb0

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: white chair, pillow, laptop, bike. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[-7, -4], [-7, 0], [-1, -2], [-1, -4]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.223

threshold: 0.900

score: 0.223

evaluation_mode: prompt_cogmap

Question 3 (ID: 9ff68c1e426c4bd7)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: airplane, pillow, suitcase, white chair, backpack.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
No Query actions were used, so exact metric coordinates weren’t directly available. I constructed a single consistent integer-grid placement that satisfies the observed relative bearings/distances and the stated “near” relations (≤2) where given, while keeping the initial agent position as (0...

✅ Evaluation Results

id: 9ff68c1e426c4bd7

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: airplane, pillow, suitcase, white chair, backpack. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[3, 0], [-7, 0], [7, -4], [-7, -4], [9, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.776

threshold: 0.900

score: 0.776

evaluation_mode: prompt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 9ce88e9c1df16a31)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: laptop, shelf, wine.
List them in th...

🤔 Assistant Answer (click to toggle)
THINK:
I need to output a global cognitive map (integers) for only the objects/doors that were observed. Since no Query() actions were used, exact coordinates aren’t uniquely determined; I’ll provide a single consistent integer placement that fits the observed relative bearings/distances and the sta...

✅ Evaluation Results

id: 9ce88e9c1df16a31

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, shelf, wine. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['wine', 'laptop', 'shelf']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 36b3f8b4d004b955)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: laptop, shelf, white chair.
List the...

🤔 Assistant Answer (click to toggle)
THINK:
I must output a global cognitive map (JSON) using the start position as origin and include only objects that were observed. Since no Query() actions were used, exact coordinates were never measured; I’ll provide one consistent integer-coordinate layout that preserves the observed relative str...

✅ Evaluation Results

id: 36b3f8b4d004b955

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, shelf, white chair. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['white chair', 'laptop', 'shelf']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 9eefbbaaede91b15)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: airplane, laptop, pillow, shelf.
Lis...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll place each observed object on an integer grid by using the initial start as (0,0), converting each observation’s relative direction (given the agent’s facing at that moment) into approximate global (x,y) offsets consistent with the reported near-relations. Then, for the rotation question...

✅ Evaluation Results

id: 9eefbbaaede91b15

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: airplane, laptop, pillow, shelf. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['pillow', 'laptop', 'shelf', 'airplane']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: dfb33925c33ad150)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (0, -3) and face north.
What is the egocentric relation of airplane?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
I will build a single global coordinate map using the initial agent position as (0,0) and +y as north, including only objects/doors that were observed. Since no Query() actions were used, coordinates are inferred to be consistent with the observed relative directions/distances and proximity c...

✅ Evaluation Results

id: dfb33925c33ad150

question: Still treat your initial position as origin (0, 0) You move to (0, -3) and face north. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: a9cd391496e7f4bd)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (-2, -2) and face east.
What is the egocentric relation of airplane?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
I must output a global JSON cognitive map (initial agent position is origin). Since no Query() was used, I assign consistent integer coordinates that best fit the observed egocentric bearings/distances and the given near-relations (e.g., suitcase east of truck in the local frame at the yellow...

✅ Evaluation Results

id: a9cd391496e7f4bd

question: Still treat your initial position as origin (0, 0) You move to (-2, -2) and face east. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-slight-left, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 4bb34213337def71)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (1, 0) and face south.
What is the egocentric relation of bike?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll build a single consistent global grid (origin at the agent’s initial position) that satisfies all observed egocentric bearings/distances and the stated proximity relations (e.g., suitcase is south of truck; bike is south of laptop; cap north of wine; chair east of wine). Then I’ll answer...

✅ Evaluation Results

id: 4bb34213337def71

question: Still treat your initial position as origin (0, 0) You move to (1, 0) and face south. What is the egocentric relation of bike? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 589844ce1a3db594)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: airplane is at front-right, slightly far, facing backward; laptop is at front-left, near, facing backward; brown door is at front-left, slightly far, on left wall
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate...

🤔 Assistant Answer (click to toggle)
THINK:
I will compile a single global (x,y) map using the initial agent position as (0,0) and only include objects/doors that were actually observed. Because no Query() actions were used, exact coordinates are reconstructed as one consistent integer layout that matches the observed relative bearings...

✅ Evaluation Results

id: 589844ce1a3db594

question: You move to a new location and face north. You observe: airplane is at front-right, slightly far, facing backward; laptop is at front-left, near, facing backward; brown door is at front-left, slightly far, on left wall Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [0, -3]

final_pos: [10, 3]

final_ori: [0, 1]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'airplane', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'laptop', 'direction': 'front-left', 'distance': 'near', 'orientation': 'facing backward'}, {'name': 'brown door', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'on left wall'}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.717

raw_score: 0.717

score: 0.717

evaluation_mode: prompt_cogmap

Question 2 (ID: ccba734e7d8b0db2)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: airplane is at front-slight-left, slightly far, facing right; laptop is at front, near, facing right; shelf is at front-slight-right, slightly far
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?

Answer ...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll compile a single global map using the initial agent position as (0,0) and include only objects/doors that were observed. Because no Query() actions were used, object coordinates can only be estimated from the binned bearings/distances and the JumpTo positions (treating near≈2, mid≈3, sli...

✅ Evaluation Results

id: ccba734e7d8b0db2

question: You move to a new location and face east. You observe: airplane is at front-slight-left, slightly far, facing right; laptop is at front, near, facing right; shelf is at front-slight-right, slightly far Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-2, -2]

final_pos: [8, 4]

final_ori: [1, 0]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'airplane', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'laptop', 'direction': 'front', 'distance': 'near', 'orientation': 'facing right'}, {'name': 'shelf', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.702

raw_score: 0.702

score: 0.702

evaluation_mode: prompt_cogmap

Question 3 (ID: 3663a671d8f461de)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: bike is at front-right, slightly far, facing backward; laptop is at front-right, mid distance, facing forward; shelf is at front-left, slightly far
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?

Answe...

🤔 Assistant Answer (click to toggle)
THINK:
I will output a single global cognitive map (origin at my initial position). Because only binned bearings/distances were provided during exploration (no Query actions), exact coordinates are underconstrained; I’m providing one internally consistent integer placement that preserves the observe...

✅ Evaluation Results

id: 3663a671d8f461de

question: You move to a new location and face south. You observe: bike is at front-right, slightly far, facing backward; laptop is at front-right, mid distance, facing forward; shelf is at front-left, slightly far Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [1, 0]

final_pos: [11, 6]

final_ori: [0, -1]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'laptop', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'shelf', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': None}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.243

raw_score: 0.243

score: 0.243

evaluation_mode: prompt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: 3af22411dc8e4653)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where white chair is relative to laptop.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from laptop (-1,-2) to white chair (-7,-4): (-6,-2) which falls in the west bin; distance sqrt(40)≈6.3 → slightly far.
FINAL ANSWER:
west, slightly far...

✅ Evaluation Results

id: 3af22411dc8e4653

question: You return to your starting position and face north. From a Top-Down map, describe where white chair is relative to laptop. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: west, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: f2b0e3268c58b4c8)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where white chair is relative to pillow.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
White chair and pillow share the same x coordinate; the chair is 4 units below the pillow, which is due south at mid distance.
FINAL ANSWER:
south, mid distance...

✅ Evaluation Results

id: f2b0e3268c58b4c8

question: You return to your starting position and face north. From a Top-Down map, describe where white chair is relative to pillow. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: south, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: ae731068ff074970)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where airplane is relative to pillow.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from pillow (-7,0) to airplane (3,0): (10,0) ⇒ east. Distance = 10 ⇒ far.
FINAL ANSWER:
east, far...

✅ Evaluation Results

id: ae731068ff074970

question: You return to your starting position and face north. From a Top-Down map, describe where airplane is relative to pillow. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: 77dc57ad53ccff4c)

❓ Evaluation Question (click to toggle)
Now you jump to airplane's direction, facing its direction.
Describe where bike is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From airplane at (3,0) facing south, bike at (-1,-4) is 4 units forward and 4 units to the right; distance √32 ≈ 5.66.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: 77dc57ad53ccff4c

question: Now you jump to airplane's direction, facing its direction. Describe where bike is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front-right, slightly far

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: ba63e01f9f8cf08f)

❓ Evaluation Question (click to toggle)
Now you jump to truck's direction, facing its direction.
Describe where backpack is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From truck (7,-2) facing east, backpack at (9,0) is 2 units ahead and 2 units to the left; distance √8 ≈ 2.83 → mid distance, direction front-left.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: ba63e01f9f8cf08f

question: Now you jump to truck's direction, facing its direction. Describe where backpack is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: fe59923b2b52093d)

❓ Evaluation Question (click to toggle)
Now you jump to airplane's direction, facing its direction.
Describe where shelf is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
After jumping to airplane at (3,0) and facing south, shelf at (3,-4) lies straight ahead 4 units away.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: fe59923b2b52093d

question: Now you jump to airplane's direction, facing its direction. Describe where shelf is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: db00fbb4ebb9f189)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that bike is front-right, slightly far, facing backward; shelf is front, mid distance; yellow door is front-left, mid distance, on left wall.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
Using the ground-truth map, if I stand at the airplane (3,0) facing south, the shelf at (3,-4) is directly in front at distance 4 (mid), the bike at (-1,-4) is front-right at distance ~5.7 (slightly far) and faces north (backward relative to me), and the yellow door at (4,-2) is front-left at...

✅ Evaluation Results

id: db00fbb4ebb9f189

question: Now you jump to an object's position, facing its direction. You observe that bike is front-right, slightly far, facing backward; shelf is front, mid distance; yellow door is front-left, mid distance, on left wall. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: airplane

final_pos: [13, 6]

final_ori: [0, -1]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'shelf', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'yellow door', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'on left wall'}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 1

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: dbd8ca2192fa7515)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that pillow is front-right, mid distance.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
From the ground-truth map, standing at the cap (-9,-2) facing north, the pillow at (-7,0) is 2 right and 2 forward (front-right) at distance √8 ≈ 2.83 (mid).
FINAL ANSWER:
cap...

✅ Evaluation Results

id: dbd8ca2192fa7515

question: Now you jump to an object's position, facing its direction. You observe that pillow is front-right, mid distance. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: cap

final_pos: [1, 4]

final_ori: [0, 1]

final_observation: [{'name': 'pillow', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': None}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 2

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 2c823d1f2d940432)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that brown door is front-left, slightly far, on front wall.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
From the bike’s position (-1, -4) facing north, the brown door at (-3, 0) is 4.47 units away (slightly far) and at about -26.6° (front-left).
FINAL ANSWER:
bike...

✅ Evaluation Results

id: 2c823d1f2d940432

question: Now you jump to an object's position, facing its direction. You observe that brown door is front-left, slightly far, on front wall. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: white chair

final_pos: [3, 2]

final_ori: [1, 0]

final_observation: [{'name': 'brown door', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 2

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: use_gt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 6d169a70c94f49de)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-180)
2. Jump to the object at front-left, slightly far.
3. Rotate(90)
4. Among objects which are front-right, slightly far to you, you jump to the 2nd from left also farthest one.
5. Rotat...

🤔 Assistant Answer (click to toggle)
THINK:
Compute final agent pose after the rotations/jumps, then measure yellow door’s bearing and Euclidean distance from that pose and bin them into the required egocentric direction and distance labels.
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: 6d169a70c94f49de

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-180) 2. Jump to the object at front-left, slightly far. 3. Rotate(90) 4. Among objects which are front-right, slightly far to you, you jump to the 2nd from left also farthest one. 5. Rotate(-180) After executing the actions, what is the ego relation of yellow door relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-slight-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 6a9b2df21e9fa7d6)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-90)
2. Jump to the object at front, mid distance.
3. Among objects which are front-left, slightly far to you, you jump to the 2nd from left also farthest one.
4. Rotate(-180)

After execut...

🤔 Assistant Answer (click to toggle)
THINK:
Track the agent’s pose through the rotations/jumps using the ground-truth coordinates, then compute white chair’s egocentric direction and binned distance from the final pose.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 6a9b2df21e9fa7d6

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front, mid distance. 3. Among objects which are front-left, slightly far to you, you jump to the 2nd from left also farthest one. 4. Rotate(-180) After executing the actions, what is the ego relation of white chair relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: b48679bb04763dd4)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-180)
2. Jump to the object at front-right, mid distance.
3. Rotate(-90)
4. Jump to the object at front, slightly far.

After executing the actions, what is the ego relation of suitcase rel...

🤔 Assistant Answer (click to toggle)
THINK:
After Rotate(-180) the agent faces south, so the “front-right, mid distance” object is the laptop at (-1,-2). After Rotate(-90) the agent faces east; the “front, slightly far” object is the yellow door at (4,-2). From (4,-2) facing east, the suitcase at (7,-4) is at a front-right bearing and ...

✅ Evaluation Results

id: b48679bb04763dd4

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-180) 2. Jump to the object at front-right, mid distance. 3. Rotate(-90) 4. Jump to the object at front, slightly far. After executing the actions, what is the ego relation of suitcase relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: ba3e02e16a5c87a1)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
suitcase is at front-left, slightly far, facing backward; truck is at front-left, mid distance, facing left; yellow door is at...

🤔 Assistant Answer (click to toggle)
THINK:
Final view matches being at the alarmclock (5,0) facing south; from start (0,0) facing north, rotate 180° then jump to alarmclock.
FINAL ANSWER:
Rotate(180), JumpTo(alarmclock)...

✅ Evaluation Results

id: ba3e02e16a5c87a1

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: suitcase is at front-left, slightly far, facing backward; truck is at front-left, mid distance, facing left; yellow door is at front-right, mid distance, on right wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [15, 6]

final_ori: [0, -1]

room_id: 3

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'yellow door'], ['rotate', -90], ['jumpto', 'alarmclock'], ['rotate', 180]]

final_observation: [{'name': 'suitcase', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}, {'name': 'yellow door', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'on right wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

error: target_not_visible

score: 0

evaluation_mode: use_gt_cogmap

Question 2 (ID: acb07825c6116973)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
airplane is at front-left, mid distance, facing backward; alarmclock is at front-right, mid distance, facing forward

What act...

🤔 Assistant Answer (click to toggle)
THINK:
To see airplane and alarmclock symmetrically front-left/front-right at mid distance with their facing reported as backward/forward, the agent must be at the yellow door position (4, -2) facing north. From the start (0,0) facing north, rotate to face east so the yellow door is in FOV, jump to ...

✅ Evaluation Results

id: acb07825c6116973

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: airplane is at front-left, mid distance, facing backward; alarmclock is at front-right, mid distance, facing forward What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [14, 4]

final_ori: [0, 1]

room_id: [1, 3]

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'initial_pos'], ['rotate', -90]]

final_observation: [{'name': 'airplane', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'alarmclock', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [14.0, 4.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 3f3f07a01338a14c)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
alarmclock is at front, mid distance, facing right; truck is at front-left, mid distance, facing backward; yellow door is at f...

🤔 Assistant Answer (click to toggle)
THINK:
Final view matches being at backpack (9,0) facing west. From start (0,0) facing north: rotate to face east (to have backpack in FOV), jump to backpack, then rotate to face west.
FINAL ANSWER:
Rotate(90), JumpTo(backpack), Rotate(180)...

✅ Evaluation Results

id: 3f3f07a01338a14c

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: alarmclock is at front, mid distance, facing right; truck is at front-left, mid distance, facing backward; yellow door is at front-slight-left, slightly far, on front wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [19, 6]

final_ori: [-1, 0]

room_id: 3

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'yellow door'], ['jumpto', 'backpack'], ['rotate', 180]]

final_observation: [{'name': 'alarmclock', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'yellow door', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [19.0, 6.0]

final_ori: [-1, 0]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 9c329788def30f4f)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: white chair, laptop, pillow, airplane.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided ground-truth cognitive map coordinates and report them in the requested object order.
FINAL ANSWER:
(-7, -4); (-1, -2); (-7, 0); (3, 0)...

✅ Evaluation Results

id: 9c329788def30f4f

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: white chair, laptop, pillow, airplane. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[-7, -4], [-1, -2], [-7, 0], [3, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 9bdf383982e77bb0)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: white chair, pillow, laptop, bike.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided reference cognitive map coordinates and report them in the requested object order.
FINAL ANSWER:
(-7, -4); (-7, 0); (-1, -2); (-1, -4)...

✅ Evaluation Results

id: 9bdf383982e77bb0

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: white chair, pillow, laptop, bike. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[-7, -4], [-7, 0], [-1, -2], [-1, -4]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 9ff68c1e426c4bd7)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: airplane, pillow, suitcase, white chair, backpack.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided ground-truth map coordinates directly (agent start is origin), and list the requested objects in the given order.
FINAL ANSWER:
(3, 0); (-7, 0); (7, -4); (-7, -4); (9, 0)...

✅ Evaluation Results

id: 9ff68c1e426c4bd7

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: airplane, pillow, suitcase, white chair, backpack. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[3, 0], [-7, 0], [7, -4], [-7, -4], [9, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 9ce88e9c1df16a31)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: laptop, shelf, wine.
List them in th...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s bearing from the origin with north as 0°. During a continuous counterclockwise rotation from 0° to -360°, objects align “directly ahead” when the agent’s heading matches their bearing (mod 360). Order is by decreasing heading encountered: wine (-113.96°), laptop (-153.43...

✅ Evaluation Results

id: 9ce88e9c1df16a31

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, shelf, wine. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['wine', 'laptop', 'shelf']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 36b3f8b4d004b955)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: laptop, shelf, white chair.
List the...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s direction from the agent (0,0). During a counterclockwise (CCW) 360° turn starting facing north, objects appear “directly ahead” in increasing CCW bearing order from north. Bearings: white chair ≈119.7°, laptop ≈153.4°, shelf ≈216.9°.
FINAL ANSWER:
white chair, laptop, s...

✅ Evaluation Results

id: 36b3f8b4d004b955

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, shelf, white chair. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['white chair', 'laptop', 'shelf']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 9eefbbaaede91b15)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: airplane, laptop, pillow, shelf.
Lis...

🤔 Assistant Answer (click to toggle)
THINK:
Compute bearings from origin (0° = north, positive clockwise). During counterclockwise rotation, headings decrease from 0° to -360°, so objects are encountered in order of decreasing equivalent heading angles.
FINAL ANSWER:
pillow, laptop, shelf, airplane...

✅ Evaluation Results

id: 9eefbbaaede91b15

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: airplane, laptop, pillow, shelf. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['pillow', 'laptop', 'shelf', 'airplane']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: dfb33925c33ad150)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (0, -3) and face north.
What is the egocentric relation of airplane?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From (0, -3) to airplane at (3, 0) is (dx=3, dy=3): 45° to the right of front and distance √18≈4.24, which is slightly far.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: dfb33925c33ad150

question: Still treat your initial position as origin (0, 0) You move to (0, -3) and face north. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: a9cd391496e7f4bd)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (-2, -2) and face east.
What is the egocentric relation of airplane?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (-2,-2) facing east; airplane at (3,0). Relative vector (5,2) gives bearing ≈ -21.8° (front-slight-left) and distance ≈ 5.39 (slightly far).
FINAL ANSWER:
front-slight-left, slightly far...

✅ Evaluation Results

id: a9cd391496e7f4bd

question: Still treat your initial position as origin (0, 0) You move to (-2, -2) and face east. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-slight-left, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 4bb34213337def71)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (1, 0) and face south.
What is the egocentric relation of bike?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From agent (1,0) to bike (-1,-4) is 2 left and 4 down; facing south makes “down” = front, and the bike is slightly to the right; distance √20 ≈ 4.47 → slightly far.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: 4bb34213337def71

question: Still treat your initial position as origin (0, 0) You move to (1, 0) and face south. What is the egocentric relation of bike? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 589844ce1a3db594)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: airplane is at front-right, slightly far, facing backward; laptop is at front-left, near, facing backward; brown door is at front-left, slightly far, on left wall
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate...

🤔 Assistant Answer (click to toggle)
THINK:
Match the relative bearings/distances to the known object coordinates. The only integer agent position with laptop (-1,-2) front-left near, airplane (3,0) front-right slightly far, and brown door (-3,0) front-left slightly far (facing north) is (0,-3).
FINAL ANSWER:
(0, -3)...

✅ Evaluation Results

id: 589844ce1a3db594

question: You move to a new location and face north. You observe: airplane is at front-right, slightly far, facing backward; laptop is at front-left, near, facing backward; brown door is at front-left, slightly far, on left wall Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [0, -3]

final_pos: [10, 3]

final_ori: [0, 1]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'airplane', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'laptop', 'direction': 'front-left', 'distance': 'near', 'orientation': 'facing backward'}, {'name': 'brown door', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'on left wall'}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: ccba734e7d8b0db2)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: airplane is at front-slight-left, slightly far, facing right; laptop is at front, near, facing right; shelf is at front-slight-right, slightly far
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?

Answer ...

🤔 Assistant Answer (click to toggle)
THINK:
Facing east means “front” is +x. From the observations, the laptop must be directly in front (due east) and near. If the agent is at (-2, -2), the laptop at (-1, -2) is 1 unit east (front, near). From (-2, -2), airplane at (3, 0) is east and slightly north (front-slight-left) at distance √29 ...

✅ Evaluation Results

id: ccba734e7d8b0db2

question: You move to a new location and face east. You observe: airplane is at front-slight-left, slightly far, facing right; laptop is at front, near, facing right; shelf is at front-slight-right, slightly far Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-2, -2]

final_pos: [8, 4]

final_ori: [1, 0]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'airplane', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'laptop', 'direction': 'front', 'distance': 'near', 'orientation': 'facing right'}, {'name': 'shelf', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 3663a671d8f461de)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: bike is at front-right, slightly far, facing backward; laptop is at front-right, mid distance, facing forward; shelf is at front-left, slightly far
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?

Answe...

🤔 Assistant Answer (click to toggle)
THINK:
Use the known object coordinates and the fact that facing south means “front” is toward −y, “right” is toward −x. The only integer agent position consistent with the front-right/front-left bearings and the mid/slightly-far distance bins is (1, 0).
FINAL ANSWER:
(1, 0)...

✅ Evaluation Results

id: 3663a671d8f461de

question: You move to a new location and face south. You observe: bike is at front-right, slightly far, facing backward; laptop is at front-right, mid distance, facing forward; shelf is at front-left, slightly far Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [1, 0]

final_pos: [11, 6]

final_ori: [0, -1]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'laptop', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'shelf', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': None}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: 3af22411dc8e4653)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where white chair is relative to laptop.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Using the mapped coordinates: laptop at (-1,-4), white chair at (-5,-6); from laptop to chair is 4 units west and 2 units south → south-west; distance √20≈4.47 → slightly far.
FINAL ANSWER:
south-west, slightly far...

✅ Evaluation Results

id: 3af22411dc8e4653

question: You return to your starting position and face north. From a Top-Down map, describe where white chair is relative to laptop. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: west, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 2 (ID: f2b0e3268c58b4c8)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where white chair is relative to pillow.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK: Use global positions: pillow (-3,-2), white chair (-5,-6). From pillow to chair is 2 west and 4 south → south-west; distance sqrt(20)≈4.47 → slightly far.
FINAL ANSWER:
south-west, slightly far...

✅ Evaluation Results

id: f2b0e3268c58b4c8

question: You return to your starting position and face north. From a Top-Down map, describe where white chair is relative to pillow. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: south, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: ae731068ff074970)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
From a Top-Down map, describe where airplane is relative to pillow.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from pillow to airplane and bin into the required cardinal direction and distance category.
FINAL ANSWER:
east, slightly far...

✅ Evaluation Results

id: ae731068ff074970

question: You return to your starting position and face north. From a Top-Down map, describe where airplane is relative to pillow. Answer format: <cardinal direction>, <distance> Example: north-west, near

action: None

answer: east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: 77dc57ad53ccff4c)

❓ Evaluation Question (click to toggle)
Now you jump to airplane's direction, facing its direction.
Describe where bike is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK: Jumping to the airplane puts me at its position and facing its facing (south). From there, bike lies ahead and to my right at about 7.2 units, which bins to slightly far.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: 77dc57ad53ccff4c

question: Now you jump to airplane's direction, facing its direction. Describe where bike is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front-right, slightly far

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: ba63e01f9f8cf08f)

❓ Evaluation Question (click to toggle)
Now you jump to truck's direction, facing its direction.
Describe where backpack is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
After jumping to the truck, I am at the truck’s position and facing east. Backpack is at (+2, +2) in global coordinates relative to me, which is forward (+x) and left (since left is +y when facing east). Distance is √8 ≈ 2.83 → mid distance.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: ba63e01f9f8cf08f

question: Now you jump to truck's direction, facing its direction. Describe where backpack is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: fe59923b2b52093d)

❓ Evaluation Question (click to toggle)
Now you jump to airplane's direction, facing its direction.
Describe where shelf is relative to you.
Answer format: <ego direction>, <distance>
Example: front-left, near
...

🤔 Assistant Answer (click to toggle)
THINK:
At airplane position facing south, shelf is 1 unit to the right and 6 units ahead; that is front-slight-right at a slightly far distance.
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: fe59923b2b52093d

question: Now you jump to airplane's direction, facing its direction. Describe where shelf is relative to you. Answer format: <ego direction>, <distance> Example: front-left, near

action: None

answer: front, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: db00fbb4ebb9f189)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that bike is front-right, slightly far, facing backward; shelf is front, mid distance; yellow door is front-left, mid distance, on left wall.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
The described view (shelf straight ahead, yellow door ahead-left on the left wall, and bike ahead-right farther away) best matches being at the central object in that room cluster: the truck.
FINAL ANSWER:
truck...

✅ Evaluation Results

id: db00fbb4ebb9f189

question: Now you jump to an object's position, facing its direction. You observe that bike is front-right, slightly far, facing backward; shelf is front, mid distance; yellow door is front-left, mid distance, on left wall. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: airplane

final_pos: [13, 6]

final_ori: [0, -1]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'shelf', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'yellow door', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'on left wall'}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 1

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: dbd8ca2192fa7515)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that pillow is front-right, mid distance.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK:
From the cap’s position (facing north), the pillow lies to the northeast at a mid distance, which appears as front-right.
FINAL ANSWER:
cap...

✅ Evaluation Results

id: dbd8ca2192fa7515

question: Now you jump to an object's position, facing its direction. You observe that pillow is front-right, mid distance. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: cap

final_pos: [1, 4]

final_ori: [0, 1]

final_observation: [{'name': 'pillow', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': None}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 2

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 2c823d1f2d940432)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that brown door is front-left, slightly far, on front wall.
Which object are you standing at?
Answer format: <object_name>
Example: lamp
...

🤔 Assistant Answer (click to toggle)
THINK: Brown door being front-left and slightly far when I am forced to face the object’s own facing direction best matches standing at the white chair (facing east), with the brown door located ahead and to the left at a slightly-far distance.
FINAL ANSWER:
white chair...

✅ Evaluation Results

id: 2c823d1f2d940432

question: Now you jump to an object's position, facing its direction. You observe that brown door is front-left, slightly far, on front wall. Which object are you standing at? Answer format: <object_name> Example: lamp

action: None

answer:

answer: white chair

final_pos: [3, 2]

final_ori: [1, 0]

final_observation: [{'name': 'brown door', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

airplane: [13.0, 6.0]

cap: [1.0, 4.0]

alarmclock: [15.0, 6.0]

bike: [9.0, 2.0]

white chair: [3.0, 2.0]

suitcase: [17.0, 2.0]

laptop: [9.0, 4.0]

shelf: [13.0, 2.0]

wine: [1.0, 2.0]

truck: [17.0, 4.0]

pillow: [3.0, 6.0]

backpack: [19.0, 6.0]

brown door: [7.0, 6.0]

yellow door: [14.0, 4.0]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

room_id: 2

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 6d169a70c94f49de)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-180)
2. Jump to the object at front-left, slightly far.
3. Rotate(90)
4. Among objects which are front-right, slightly far to you, you jump to the 2nd from left also farthest one.
5. Rotat...

🤔 Assistant Answer (click to toggle)
THINK: After the sequence, I end up positioned such that the yellow door is in my front-right quadrant at a slightly-far distance.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: 6d169a70c94f49de

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-180) 2. Jump to the object at front-left, slightly far. 3. Rotate(90) 4. Among objects which are front-right, slightly far to you, you jump to the 2nd from left also farthest one. 5. Rotate(-180) After executing the actions, what is the ego relation of yellow door relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-slight-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 2 (ID: 6a9b2df21e9fa7d6)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-90)
2. Jump to the object at front, mid distance.
3. Among objects which are front-left, slightly far to you, you jump to the 2nd from left also farthest one.
4. Rotate(-180)

After execut...

🤔 Assistant Answer (click to toggle)
THINK: After the rotations/jumps, the agent ends up at wine and then faces east; white chair is 1 unit directly in front (east) of that position, so it is front and near.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 6a9b2df21e9fa7d6

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front, mid distance. 3. Among objects which are front-left, slightly far to you, you jump to the 2nd from left also farthest one. 4. Rotate(-180) After executing the actions, what is the ego relation of white chair relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: b48679bb04763dd4)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will execute the following action sequence:
1. Rotate(-180)
2. Jump to the object at front-right, mid distance.
3. Rotate(-90)
4. Jump to the object at front, slightly far.

After executing the actions, what is the ego relation of suitcase rel...

🤔 Assistant Answer (click to toggle)
THINK:
After the action sequence you end up at the yellow door facing east; from that viewpoint earlier, the suitcase was observed at front-right and mid distance.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: b48679bb04763dd4

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-180) 2. Jump to the object at front-right, mid distance. 3. Rotate(-90) 4. Jump to the object at front, slightly far. After executing the actions, what is the ego relation of suitcase relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: ba3e02e16a5c87a1)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
suitcase is at front-left, slightly far, facing backward; truck is at front-left, mid distance, facing left; yellow door is at...

🤔 Assistant Answer (click to toggle)
THINK: To see truck and suitcase from a non-door location while also seeing the yellow door, the agent must be standing at the backpack (so truck/suitcase can both be in view) and be facing south. From the start, rotate to bring the yellow door into view, jump to it, jump to the backpack (visible fr...

✅ Evaluation Results

id: ba3e02e16a5c87a1

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: suitcase is at front-left, slightly far, facing backward; truck is at front-left, mid distance, facing left; yellow door is at front-right, mid distance, on right wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [15, 6]

final_ori: [0, -1]

room_id: 3

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'yellow door'], ['rotate', -90], ['jumpto', 'alarmclock'], ['rotate', 180]]

final_observation: [{'name': 'suitcase', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}, {'name': 'yellow door', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'on right wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [19.0, 6.0]

final_ori: [0, -1]

visible_match: False

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: acb07825c6116973)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
airplane is at front-left, mid distance, facing backward; alarmclock is at front-right, mid distance, facing forward

What act...

🤔 Assistant Answer (click to toggle)
THINK:
To get a view where airplane is front-left and alarmclock is front-right, the simplest is to move into the connecting doorway (yellow door) and then face north from there. From the start (facing north), rotate east to bring the yellow door into view, jump to it, then rotate back to north.
FIN...

✅ Evaluation Results

id: acb07825c6116973

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: airplane is at front-left, mid distance, facing backward; alarmclock is at front-right, mid distance, facing forward What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [14, 4]

final_ori: [0, 1]

room_id: [1, 3]

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'initial_pos'], ['rotate', -90]]

final_observation: [{'name': 'airplane', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'alarmclock', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [14.0, 4.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 3f3f07a01338a14c)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
Then you have executed an action sequence and changed to a new location and facing direction.
You observe the following:
alarmclock is at front, mid distance, facing right; truck is at front-left, mid distance, facing backward; yellow door is at f...

🤔 Assistant Answer (click to toggle)
THINK:
From the start (facing north), rotate to bring the yellow door into view, jump to it, then jump deeper into the connected room to the backpack (which is visible from the doorway). Finally rotate to face back toward the yellow door so it appears on the front wall along with the truck and newly...

✅ Evaluation Results

id: 3f3f07a01338a14c

question: You return to your starting position and face north. Then you have executed an action sequence and changed to a new location and facing direction. You observe the following: alarmclock is at front, mid distance, facing right; truck is at front-left, mid distance, facing backward; yellow door is at front-slight-left, slightly far, on front wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [19, 6]

final_ori: [-1, 0]

room_id: 3

init_pos: [10, 6]

init_ori: [0, 1]

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

minimal_plan: [['rotate', 90], ['jumpto', 'yellow door'], ['jumpto', 'backpack'], ['rotate', 180]]

final_observation: [{'name': 'alarmclock', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'yellow door', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [19.0, 6.0]

final_ori: [-1, 0]

visible_match: True

score: 1

evaluation_mode: use_model_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 9c329788def30f4f)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: white chair, laptop, pillow, airplane.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Use the global cognitive map coordinates previously constructed relative to the initial origin (0,0), with x east/right and y north/up, and report them in the requested object order.
FINAL ANSWER:
(-5, -6); (-1, -4); (-3, -2); (3, 0)...

✅ Evaluation Results

id: 9c329788def30f4f

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: white chair, laptop, pillow, airplane. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[-7, -4], [-1, -2], [-7, 0], [3, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.503

threshold: 0.900

score: 0.503

evaluation_mode: use_model_cogmap

Question 2 (ID: 9bdf383982e77bb0)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: white chair, pillow, laptop, bike.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Use the previously constructed global cognitive map coordinates (allocentric, origin at start, +x east, +y north) for the requested objects in the given order.
FINAL ANSWER:
(-5, -6); (-3, -2); (-1, -4); (-1, -6)...

✅ Evaluation Results

id: 9bdf383982e77bb0

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: white chair, pillow, laptop, bike. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[-7, -4], [-7, 0], [-1, -2], [-1, -4]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.549

threshold: 0.900

score: 0.549

evaluation_mode: use_model_cogmap

Question 3 (ID: 9ff68c1e426c4bd7)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: airplane, pillow, suitcase, white chair, backpack.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK: Use the established global cognitive map coordinates (origin at initial position, x east, y north) for the requested objects in the given order.
FINAL ANSWER:
(3, 0); (-3, -2); (7, -6); (-5, -6); (9, -2)...

✅ Evaluation Results

id: 9ff68c1e426c4bd7

question: Treat your starting position as the origin (0, 0) while facing north. Report allocentric coordinates using (x right/east, y up/north). Objects: airplane, pillow, suitcase, white chair, backpack. Answer format: (x0, y0); (x1, y1); ... in the same order. Example: (1, 0); (-2, 3); (0, -1)

action: None

answer: [[3, 0], [-7, 0], [7, -4], [-7, -4], [9, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.747

threshold: 0.900

score: 0.747

evaluation_mode: use_model_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 9ce88e9c1df16a31)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: laptop, shelf, wine.
List them in th...

🤔 Assistant Answer (click to toggle)
THINK: Compute each object’s bearing from origin (facing north) and order by when the heading matches that bearing during a counterclockwise rotation (0 down to -360). Wine (-135°) appears first, then laptop (~-166°), then shelf (equiv. -198°).
FINAL ANSWER:
wine, laptop, shelf...

✅ Evaluation Results

id: 9ce88e9c1df16a31

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, shelf, wine. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['wine', 'laptop', 'shelf']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 36b3f8b4d004b955)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: laptop, shelf, white chair.
List the...

🤔 Assistant Answer (click to toggle)
THINK: Using the inferred global coordinates from the built map relative to the start (0,0), compute each target’s bearing from north, then order by when the agent’s heading matches those bearings during a continuous counterclockwise (left) rotation from 0° down through -360°.
FINAL ANSWER:
white ch...

✅ Evaluation Results

id: 36b3f8b4d004b955

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, shelf, white chair. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['white chair', 'laptop', 'shelf']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 9eefbbaaede91b15)

❓ Evaluation Question (click to toggle)
You return to your starting position and face north.
You will perform a full 360-degree rotation by continuously turning counterclockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: airplane, laptop, pillow, shelf.
Lis...

🤔 Assistant Answer (click to toggle)
THINK: Compute bearings from origin (0,0) facing north using each object’s inferred global coordinates; during counterclockwise rotation, objects are encountered in order of decreasing bearing from 0 down to -360.
FINAL ANSWER:
pillow, laptop, shelf, airplane...

✅ Evaluation Results

id: 9eefbbaaede91b15

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: airplane, laptop, pillow, shelf. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['pillow', 'laptop', 'shelf', 'airplane']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: dfb33925c33ad150)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (0, -3) and face north.
What is the egocentric relation of airplane?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK: From agent (0,-3) facing north, airplane at (3,0) is at vector (+3,+3): 45° to the right of front and distance √18≈4.24.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: dfb33925c33ad150

question: Still treat your initial position as origin (0, 0) You move to (0, -3) and face north. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: a9cd391496e7f4bd)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (-2, -2) and face east.
What is the egocentric relation of airplane?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From (-2,-2) facing east, airplane at (3,0) is 5 units ahead and 2 units to the left; angle ≈ 21.8° left of front and distance ≈ 5.4.
FINAL ANSWER:
front-slight-left, slightly far...

✅ Evaluation Results

id: a9cd391496e7f4bd

question: Still treat your initial position as origin (0, 0) You move to (-2, -2) and face east. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-slight-left, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 4bb34213337def71)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (1, 0) and face south.
What is the egocentric relation of bike?

Answer format: <direction>, <distance>
Example: front, near
...

🤔 Assistant Answer (click to toggle)
THINK:
From agent at (1,0) facing south, bike at (-1,-6) is 6 forward and 2 to the right in the agent frame; distance √40≈6.3.
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: 4bb34213337def71

question: Still treat your initial position as origin (0, 0) You move to (1, 0) and face south. What is the egocentric relation of bike? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 589844ce1a3db594)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: airplane is at front-right, slightly far, facing backward; laptop is at front-left, near, facing backward; brown door is at front-left, slightly far, on left wall
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate...

🤔 Assistant Answer (click to toggle)
THINK: Using the previously established global coordinates airplane (3, 0), laptop (-1, -4), and brown door (-3, -2), the only integer agent position that makes the laptop front-left at near distance and also makes the airplane and brown door front-right/front-left at slightly-far distances is (0, -...

✅ Evaluation Results

id: 589844ce1a3db594

question: You move to a new location and face north. You observe: airplane is at front-right, slightly far, facing backward; laptop is at front-left, near, facing backward; brown door is at front-left, slightly far, on left wall Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [0, -3]

final_pos: [10, 3]

final_ori: [0, 1]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'airplane', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'laptop', 'direction': 'front-left', 'distance': 'near', 'orientation': 'facing backward'}, {'name': 'brown door', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'on left wall'}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.513

raw_score: 0.513

score: 0.513

evaluation_mode: use_model_cogmap

Question 2 (ID: ccba734e7d8b0db2)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: airplane is at front-slight-left, slightly far, facing right; laptop is at front, near, facing right; shelf is at front-slight-right, slightly far
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?

Answer ...

🤔 Assistant Answer (click to toggle)
THINK: Facing east means “front” is +x. Laptop being at front, near implies I’m 1–2 units west of the laptop on the same y. The shelf being front-slight-right and slightly far best matches being at (-3, -3) among consistent integer candidates.
FINAL ANSWER:
(-3, -3)...

✅ Evaluation Results

id: ccba734e7d8b0db2

question: You move to a new location and face east. You observe: airplane is at front-slight-left, slightly far, facing right; laptop is at front, near, facing right; shelf is at front-slight-right, slightly far Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-2, -2]

final_pos: [8, 4]

final_ori: [1, 0]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'airplane', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'laptop', 'direction': 'front', 'distance': 'near', 'orientation': 'facing right'}, {'name': 'shelf', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.607

raw_score: 0.607

score: 0.607

evaluation_mode: use_model_cogmap

Question 3 (ID: 3663a671d8f461de)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: bike is at front-right, slightly far, facing backward; laptop is at front-right, mid distance, facing forward; shelf is at front-left, slightly far
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?

Answe...

🤔 Assistant Answer (click to toggle)
THINK: Facing south means front is toward negative y. “Front-right” places bike/laptop to the southwest of me; “front-left” places shelf to the southeast. Using the consistent integer map positions bike (0,-5), laptop (0,-3), shelf (4,-5), the unique integer agent position that fits the distance bin...

✅ Evaluation Results

id: 3663a671d8f461de

question: You move to a new location and face south. You observe: bike is at front-right, slightly far, facing backward; laptop is at front-right, mid distance, facing forward; shelf is at front-left, slightly far Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [1, 0]

final_pos: [11, 6]

final_ori: [0, -1]

room_id: 1

object_positions:

airplane: [13, 6]

cap: [1, 4]

alarmclock: [15, 6]

bike: [9, 2]

white chair: [3, 2]

suitcase: [17, 2]

laptop: [9, 4]

shelf: [13, 2]

wine: [1, 2]

truck: [17, 4]

pillow: [3, 6]

backpack: [19, 6]

brown door: [7, 6]

yellow door: [14, 4]

object_orientations:

airplane: [0, -1]

cap: [0, 1]

alarmclock: [0, 1]

bike: [0, 1]

white chair: [1, 0]

suitcase: [0, 1]

laptop: [0, -1]

truck: [1, 0]

backpack: [0, 1]

brown door: [1, 0]

yellow door: [1, 0]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'laptop', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'shelf', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': None}]

gate_info:

brown door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

object_rooms:

airplane: 1

cap: 2

alarmclock: 3

bike: 1

white chair: 2

suitcase: 3

laptop: 1

shelf: 1

wine: 2

truck: 3

pillow: 2

backpack: 3

brown door: [2, 1]

yellow door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.243

raw_score: 0.243

score: 0.243

evaluation_mode: use_model_cogmap

Model: gpt-5.2

sample_run07

Select Configuration:

📊 Sample Metrics

🔍 Exploration

✅ Evaluation

✅ Evaluation (prompt_cogmap)

✅ Evaluation (use_gt_cogmap)

✅ Evaluation (use_model_cogmap)

🧠 Cognitive Map

🧭 False Belief CogMap

📊 Performance Charts

Cognitive Map (Update)

Cognitive Map (Full)

Cognitive Map (Self-Tracking)

FB CogMap (Unchanged)

Fog Probe F1

Fog Probe Precision

Fog Probe Recall

Position Update

Facing Update

Position Stability

Facing Stability

🌍 Exploration Phase

🔄 Turn 1

🔄 Turn 2

🔄 Turn 3

🔄 Turn 4

🔄 Turn 5

🔄 Turn 6

🔄 Turn 7

🔄 Turn 8

🔄 Turn 9

🔄 Turn 10

🔄 Turn 11

🔄 Turn 12

🔄 Turn 13

🔄 Turn 14

🔄 Turn 15

🔄 Turn 16

🔄 Turn 17

🔄 Turn 18

🔄 Turn 19

🔄 Turn 20

🔄 Turn 21

🧭 False Belief Exploration

🔄 FB Turn 22

🔄 FB Turn 23

🔄 FB Turn 24

🔄 FB Turn 25

🔄 FB Turn 26

🔄 FB Turn 27

🔄 FB Turn 28

📊 Evaluation (default)

📊 Task: DirectionEvaluationTask

Question 1 (ID: 3af22411dc8e4653)

Question 2 (ID: f2b0e3268c58b4c8)

Question 3 (ID: ae731068ff074970)

📊 Task: PovEvaluationTask

Question 1 (ID: 77dc57ad53ccff4c)

Question 2 (ID: ba63e01f9f8cf08f)

Question 3 (ID: fe59923b2b52093d)

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: db00fbb4ebb9f189)

Question 2 (ID: dbd8ca2192fa7515)

Question 3 (ID: 2c823d1f2d940432)

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 6d169a70c94f49de)

Question 2 (ID: 6a9b2df21e9fa7d6)

Question 3 (ID: b48679bb04763dd4)

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: ba3e02e16a5c87a1)

Question 2 (ID: acb07825c6116973)

Question 3 (ID: 3f3f07a01338a14c)

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 9c329788def30f4f)

Question 2 (ID: 9bdf383982e77bb0)

Question 3 (ID: 9ff68c1e426c4bd7)

📊 Task: RotEvaluationTask

Question 1 (ID: 9ce88e9c1df16a31)