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 position (origin) while facing north to discover visible objects/doors and their relationships, enabling efficient next moves....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assign integer coordinates consistent with egocentric bearings/distances from agent at origin facing north; include object facings as cardinal directions.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "north" },
"scooter": { "position": [1, 3], "facing": "east" },
"teddybear"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      5
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Convert the binned egocentric bearings/distances into a consistent set of integer relative coordinates in the agent’s local frame (+y forward, +x right), and encode observed facings in local axes.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"scooter": {
"position": [1, 3],
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "scooter": {
      "position": [
        1,
        3
      ],
      "facing": "+x"
    },
    "teddybear": {
      "position": [
        -3,
        5
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "scooter": {
      "position": [
        1,
        3
      ],
      "facing": "+x"
    },
    "teddybear": {
      "position": [
        -3,
        5
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: Observed region is the agent’s current 90° FOV facing north; candidates behind or to the side (outside FOV) are unexplored. A, E (south/behind) and F (west/left) are outside the last observed wedge, while B/C/D are ahead within it.
FINAL ANSWER:
{"unexplored":["A","E","F"]}...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#....DB#
#......#...C..#
#......+.F..*.#
#......#..A.E.#
#......#......#
###########+###
######......###
######......###
######......###
######......###
######......###
######......###
###############

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, E, F

🎯 Ground Truth: E, F, A

🧠 Cognitive Map Metrics

Global:

dir: 0.833

facing: 1

pos: 0.860

overall: 0.898

Global (Full):

dir: 0.042

facing: 0.250

pos: 0.172

overall: 0.155

Local:

dir: 0.667

facing: 1

pos: 0.831

overall: 0.833

Local (Newly):

dir: 0.667

facing: 1

pos: 0.831

overall: 0.833

Fog Probe:

precision: 1

recall: 1

overall: 1

📈 Turn Metrics

node_coverage: 0.143

edge_coverage: 0.049

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: 0.111

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 2

👤 Environment Observation (click to toggle)
You observe:
• scooter: front-slight-right, mid distance, facing right
• teddybear: front-left, slightly far, facing right.
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)
Current FOV only revealed two objects; to expand coverage cheaply, rotate to scan a new quadrant from the same spot. Rotate 90° clockwise (face east) and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need to place observed objects on integer grid consistent with relative bearings/distances; keep agent at origin, update facing after rotation.
FINAL ANSWER: {
"agent": {
"position": [0, 0],
"facing": "east"
},
"scooter": {
"position": [1, 3],
"facing": "east"
},
"te...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -2,
      4
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: No objects are currently in my field of view after rotating; local 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)

🌫️ Fog Probe Response (click to toggle)
THINK: We have only observed a small portion of the current room (north-facing view). All candidate points A-F lie outside our observed FOVs and/or in other rooms beyond doors, so they are in unexplored regions.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: C, D, F

🧠 Cognitive Map Metrics

Global:

dir: 1

facing: 1

pos: 0.899

overall: 0.966

Global (Full):

dir: 0.050

facing: 0.250

pos: 0.180

overall: 0.160

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 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: 0.111

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 3

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe: No objects in field of view..
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)
Continue a low-cost scan from the origin by rotating to the next quadrant. Rotate 90° clockwise (now facing south) and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a consistent integer-coordinate global map (origin at initial agent position), using observed relative bearings/distances from the origin across rotations; include facing for objects that have it and for the agent.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "south" },
"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "south"
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      5
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      2,
      -2
    ]
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) map for the two currently visible items, placing them at integer coordinates consistent with their relative bearings (front-left vs front-slight-right) and mid-distance.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"plant": { "position": [-1, 3] },
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "plant": {
      "position": [
        -1,
        3
      ]
    },
    "brown door": {
      "position": [
        1,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "plant": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      1,
      3
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "plant": {
      "position": [
        -1,
        3
      ]
    },
    "brown door": {
      "position": [
        1,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "plant": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      1,
      3
    ],
    "facing": "north"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: C, D, and E are in the upper/right sections that have not been covered by our limited FOV observations so far, while A/B/F are near the currently explored corridor/door area.
FINAL ANSWER: {"unexplored":["C","D","E"]}...

🗺️ Symbolic Fog Map

###############
#......#.C....#
#......#.....D#
#......#......#
#......+.F.A*.#
#......#....B.#
#......#.E....#
###########+###
######......###
######......###
######......###
######......###
######......###
######......###
###############

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: C, D, E

🎯 Ground Truth: A, F, E

🧠 Cognitive Map Metrics

Global:

dir: 0.867

facing: 1

pos: 0.877

overall: 0.915

Global (Full):

dir: 0.108

facing: 0.250

pos: 0.292

overall: 0.217

Local:

dir: 0.333

facing: 1

pos: 0.912

overall: 0.748

Local (Newly):

dir: 0.333

facing: 1

pos: 0.912

overall: 0.748

Fog Probe:

precision: 0.333

recall: 0.333

overall: 0.333

📈 Turn Metrics

node_coverage: 0.286

edge_coverage: 0.098

is_action_fail: False

step: 3

action_counts:

move: 0

rotate: 2

return: 0

observe: 3

term: 0

forced_term: 0

query: 0

information_gain: 0.240

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 4

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• plant: front-left, mid distance
• brown door: front-slight-right, mid distance, on front wall.
You have a maximum of 17 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 brown door to stand in the doorway and gain visibility into both connected rooms in a single observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assign integer coordinates consistent with observed egocentric bearings/distances across rotations; treat doorway observation with global agent facing south; satisfy reported proximity relations by placing cap and toilet within 2 units of green chair.
FINAL ANSWER:
{
"agent": { "position":...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      5
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -3
    ]
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -4,
      -8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) map from the current doorway view; assign integer coordinates consistent with observed bearings/distances and given proximity relations (chair near cap; chair near toilet).
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"green chair": {
"position"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green chair": {
      "position": [
        0,
        4
      ],
      "facing": "-x"
    },
    "cap": {
      "position": [
        0,
        6
      ],
      "facing": "+y"
    },
    "toilet": {
      "position": [
        2,
        4
      ],
      "facing": "+y"
    },
    "television": {
      "position": [
        3,
        5
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "toilet": {
    "position": [
      2,
      4
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      0,
      6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      4,
      6
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green chair": {
      "position": [
        0,
        4
      ],
      "facing": "-x"
    },
    "cap": {
      "position": [
        0,
        6
      ],
      "facing": "+y"
    },
    "toilet": {
      "position": [
        2,
        4
      ],
      "facing": "+y"
    },
    "television": {
      "position": [
        3,
        5
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "toilet": {
    "position": [
      2,
      4
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      0,
      6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      4,
      6
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: Unseen candidates appear across upper-right and lower rooms; none were observed in any prior FOV, so all labeled points are in unexplored/unobserved regions.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"]
}...

🗺️ Symbolic Fog Map

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

🔍 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: J, E, B, G, F, L

🧠 Cognitive Map Metrics

Global:

dir: 0.867

facing: 1

pos: 0.891

overall: 0.919

Global (Full):

dir: 0.325

facing: 0.583

pos: 0.535

overall: 0.481

Local:

dir: 0.900

facing: 1

pos: 0.912

overall: 0.937

Local (Newly):

dir: 0.900

facing: 1

pos: 0.912

overall: 0.937

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.571

edge_coverage: 0.195

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: 0.485

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 5

👤 Environment Observation (click to toggle)
You jumped to brown door. You observe:
• green chair: front, mid distance, facing left
• cap: front, slightly far, facing forward
• toilet: front-right, slightly far, facing forward
• television: front-right, slightly far, facing backward
Assume your current facing direction is called "north":
• gr...

🤔 Assistant Thinking (click to toggle)
While in the doorway, rotate to look back into the other connected room to potentially reveal the remaining door (red door) and any unseen objects, leveraging the doorway’s dual-room visibility....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Infer integer coordinates from binned bearings/distances across observations; set initial agent at [0,0], track rotations/jump to place brown door and all seen objects consistently; include facing only where reported.
FINAL ANSWER:
{
"agent": { "position": [-2, -2], "facing": "north" },
"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      -2
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -2,
      -2
    ]
  },
  "plant": {
    "position": [
      2,
      -2
    ]
  },
  "green chair": {
    "position": [
      -3,
      -5
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -3,
      -7
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -5,
      -5
    ],
    "facing": "north"
  },
  "television": {
    "position": [
      -6,
      -4
    ],
    "facing": "south"
  },
  "scooter": {
    "position": [
      2,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -5,
      5
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -6,
      4
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) map from the current FOV while facing +y; place objects at approximate integer coordinates consistent with their angle (left/right) and distance bin (slightly far).
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"scooter": { "position": [1, 6],...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "scooter": {
      "position": [
        1,
        6
      ],
      "facing": "+x"
    },
    "truck": {
      "position": [
        -2,
        6
      ],
      "facing": "+x"
    },
    "teddybear": {
      "position": [
        -1,
        6
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "teddybear": {
    "position": [
      -2,
      6
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -2,
      4
    ],
    "facing": "east"
  },
  "scooter": {
    "position": [
      2,
      6
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "scooter": {
      "position": [
        1,
        6
      ],
      "facing": "+x"
    },
    "truck": {
      "position": [
        -2,
        6
      ],
      "facing": "+x"
    },
    "teddybear": {
      "position": [
        -1,
        6
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "truck": {
    "position": [
      -2,
      4
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: From the doorway we have visually covered the forward (south) cone into the lower room and the forward (north) cone into the upper-right room; the candidate points that fall outside those observed 90° cones (more to the southwest than our south-facing view) remain unobserved.
FINAL ANSWER: {
...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#......#
#......#..D...#
#......+...I.C#
#......#E.....#
#......#HF....#
###########*###
######......###
######J.L...###
######.B....###
######......###
######......###
######.KG..A###
###############

🔍 Fog Probe JSONs

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

🤖 Predicted: B, J, L

🎯 Ground Truth: E, F, H, B, J, L

🧠 Cognitive Map Metrics

Global:

dir: 0.582

facing: 0.500

pos: 0.687

overall: 0.590

Global (Full):

dir: 0.267

facing: 0.333

pos: 0.458

overall: 0.353

Local:

dir: 0.500

facing: 1

pos: 0.831

overall: 0.777

Local (Newly):

dir: 0

facing: 1

pos: 0.769

overall: 0.590

Fog Probe:

precision: 1

recall: 0.500

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.268

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: 0.588

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 6

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• scooter: front-slight-right, slightly far, facing right
• truck: front-left, slightly far, facing right
• teddybear: front-slight-left, slightly far, facing right
Assume your current facing direction is called "north":
• truck is south, near to teddybear.
...

🤔 Assistant Thinking (click to toggle)
We’re still positioned in the brown doorway, which is a high-value vantage point (can see into both rooms). We’ve seen room1 and part of room2 but still haven’t found the red door or several objects. Rotate 90° to scan a new sector from the doorway and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a single consistent global placement (integer grid) for all previously observed entities, using the initial agent pose as origin and cardinal facings; approximate positions to best fit the binned bearings/distances and doorway jump.
FINAL ANSWER:
{
"agent": { "position": [-2, -3], "fa...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      -3
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -2,
      -3
    ]
  },
  "scooter": {
    "position": [
      2,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ]
  },
  "green chair": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -3,
      -8
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -4,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Construct a local (agent-centric) map from the current FOV: only the plant is visible, positioned front-left at a mid distance; no facing information was observed for it.
FINAL ANSWER: {
"origin": "agent",
"objects": {
"plant": {
"position": [-2, 3]
}
}
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "plant": {
      "position": [
        -2,
        3
      ]
    }
  }
}

🎯 Ground Truth

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

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "plant": {
      "position": [
        -2,
        3
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Areas observed so far are concentrated near the brown door and nearby floor space; the candidate point B appears closest to that already-observed vicinity, while the remaining labeled candidates lie deeper into regions not yet observed (including the upper-right area beyond the interior door ...

🗺️ Symbolic Fog Map

###############
#......#..J..C#
#......#......#
#......#H.....#
#......+......#
#......#D.....#
#......#IF....#
###########*###
######..B...###
######A.....###
######.G....###
######.....L###
######E.....###
######.K....###
###############

🔍 Fog Probe JSONs

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

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

🎯 Ground Truth: D, I, F, B, A, G

🧠 Cognitive Map Metrics

Global:

dir: 0.673

facing: 1

pos: 0.831

overall: 0.835

Global (Full):

dir: 0.308

facing: 0.667

pos: 0.554

overall: 0.510

Local:

dir: 1

facing: 1

pos: 0.831

overall: 0.944

Local (Newly):

(none)

Fog Probe:

precision: 0.455

recall: 0.833

overall: 0.588

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.293

is_action_fail: False

step: 6

action_counts:

move: 1

rotate: 4

return: 0

observe: 6

term: 0

forced_term: 0

query: 0

information_gain: 0.602

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 7

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• plant: front-left, mid distance.
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)
From the brown doorway, continue the 360° scan to find the remaining door (red door) and unseen objects. Rotate 90° clockwise to face a new sector and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single global (origin-at-start) integer-coordinate map consistent with all observed items, including agent’s current pose and object facings when provided.
FINAL ANSWER:
```json
{
"agent": { "position": [-2, -3], "facing": "south" },
"brown door": { "position": [-2, -3] },
"sco...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      -3
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      -2,
      -3
    ]
  },
  "scooter": {
    "position": [
      1,
      4
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      2,
      -3
    ]
  },
  "green chair": {
    "position": [
      -2,
      -7
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -2,
      -9
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -4,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Construct a locally consistent integer-coordinate layout matching each object’s egocentric bearing/distance bins and the given near (≤2) cardinal relations (chair south of cap; chair west of toilet).
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"green chair": {
"position": [...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green chair": {
      "position": [
        0,
        4
      ],
      "facing": "-x"
    },
    "cap": {
      "position": [
        0,
        6
      ],
      "facing": "+y"
    },
    "toilet": {
      "position": [
        2,
        4
      ],
      "facing": "+y"
    },
    "television": {
      "position": [
        3,
        5
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "toilet": {
    "position": [
      2,
      4
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      0,
      6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      4,
      6
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green chair": {
      "position": [
        0,
        4
      ],
      "facing": "-x"
    },
    "cap": {
      "position": [
        0,
        6
      ],
      "facing": "+y"
    },
    "toilet": {
      "position": [
        2,
        4
      ],
      "facing": "+y"
    },
    "television": {
      "position": [
        3,
        5
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Explored regions correspond to the area around the brown doorway and the adjacent room containing (green chair/cap/toilet/television), aligning with central candidates (F/G/B). The top-right pocket (A/L/J) and the lower open region (C/D/I/E/K/H) remain unobserved and likely belong to other ro...

🗺️ Symbolic Fog Map

###############
#......#...ALJ#
#......#......#
#......#......#
#......+......#
#......#F.....#
#......#GB....#
###########*###
######C.DI..###
######......###
######....E.###
######...K..###
######.....H###
######......###
###############

🔍 Fog Probe JSONs

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

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

🎯 Ground Truth: B, G, F, D, I, C

🧠 Cognitive Map Metrics

Global:

dir: 0.745

facing: 1

pos: 0.856

overall: 0.867

Global (Full):

dir: 0.342

facing: 0.667

pos: 0.571

overall: 0.526

Local:

dir: 0.900

facing: 1

pos: 0.912

overall: 0.937

Local (Newly):

(none)

Fog Probe:

precision: 0.333

recall: 0.500

overall: 0.400

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.293

is_action_fail: False

step: 7

action_counts:

move: 1

rotate: 5

return: 0

observe: 7

term: 0

forced_term: 0

query: 0

information_gain: 0.602

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 8

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• green chair: front, mid distance, facing left
• cap: front, slightly far, facing forward
• toilet: front-right, slightly far, facing forward
• television: front-right, slightly far, facing backward
Assume your current facing direction is called "north":
• g...

🤔 Assistant Thinking (click to toggle)
We still haven’t located the red door or the remaining objects (shoppingcart, airplane, laptop, backpack). Move deeper into this room by jumping to a central visible object (green chair) to change vantage and hopefully reveal the red door and new items....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assign integer coordinates consistent with all observed relative bearings/distances across rotations/jumps; keep only observed entities and include facing where given.
FINAL ANSWER:
{
"agent": { "position": [-1, -7], "facing": "south" },
"brown door": { "position": [-1, -3] },
"plant": ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -7
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  },
  "plant": {
    "position": [
      1,
      -2
    ]
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -4,
      -8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -7
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -7
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local map centered on the agent at the green chair; only the cap is currently in FOV, positioned in front at near distance and facing forward.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"cap": {
"position": [0, 1],
"facing": "+y"
}
}
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        0,
        1
      ],
      "facing": "+y"
    }
  }
}

🎯 Ground Truth

{
  "cap": {
    "position": [
      0,
      2
    ],
    "facing": "south"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "cap": {
      "position": [
        0,
        1
      ],
      "facing": "+y"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Most candidate points lie in areas/rooms we have not yet visually covered from our limited observations near the brown door/green chair; the only ones plausibly within the currently explored vicinity are K and F near the agent.
FINAL ANSWER:
```json
{
"unexplored": ["A", "B", "C", "D", "E"...

🗺️ Symbolic Fog Map

###############
#......#...B..#
#......#......#
#......#......#
#......+......#
#......#LG..D.#
#......#AI....#
###########+###
######......###
######CHE...###
######...J..###
######..K.F*###
######......###
######......###
###############

🔍 Fog Probe JSONs

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

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

🎯 Ground Truth: L, A, I, E, C, H

🧠 Cognitive Map Metrics

Global:

dir: 0.964

facing: 1

pos: 0.943

overall: 0.969

Global (Full):

dir: 0.442

facing: 0.667

pos: 0.629

overall: 0.579

Local:

dir: 1

facing: 1

pos: 0.877

overall: 0.959

Local (Newly):

(none)

Fog Probe:

precision: 0.600

recall: 1

overall: 0.750

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.317

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: 0.602

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 9

👤 Environment Observation (click to toggle)
You jumped to green chair. You observe:
• cap: front, near, facing forward.
You have a maximum of 12 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
From the green chair, only the cap is currently in view, so I should scan other directions from this new vantage to discover additional objects or the red door. Rotate 90° clockwise and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single consistent global coordinate map (origin at initial agent position), converting egocentric observations into approximate integer coordinates and converting object-facing (left/right/forward/backward) into global cardinal directions based on agent orientation at time of observat...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      1,
      -6
    ],
    "facing": "west"
  },
  "brown door": {
    "position": [
      1,
      -2
    ]
  },
  "plant": {
    "position": [
      3,
      -2
    ]
  },
  "scooter": {
    "position": [
      2,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -1,
      5
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      1,
      -6
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      1,
      -7
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -1,
      -6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -3,
      -8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -7
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -7
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Convert current egocentric observations into an agent-centric (+y forward) integer-coordinate map; encode object facings on local axes.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"toilet": {
"position": [0, 1],
"facing": "-x"
},
"television": {
"position...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "toilet": {
      "position": [
        0,
        1
      ],
      "facing": "-x"
    },
    "television": {
      "position": [
        -1,
        5
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "toilet": {
    "position": [
      0,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -2,
      4
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "toilet": {
      "position": [
        0,
        1
      ],
      "facing": "-x"
    },
    "television": {
      "position": [
        -1,
        5
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A, B, C, D, E, H (upper rooms) and F, G (lower room areas away from current observed cone) all lie in regions we have not yet directly observed/covered, so they should be marked unexplored.
FINAL ANSWER:
{"unexplored":["A","B","C","D","E","F","G","H"]}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F, G, H

🤖 Predicted: A, B, C, D, E, F, G, H

🎯 Ground Truth: D, B, A, F

🧠 Cognitive Map Metrics

Global:

dir: 0.782

facing: 1

pos: 0.738

overall: 0.840

Global (Full):

dir: 0.358

facing: 0.667

pos: 0.492

overall: 0.506

Local:

dir: 0.333

facing: 1

pos: 0.852

overall: 0.728

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.366

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: 0.625

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 10

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• toilet: front, near, facing left
• television: front-left, slightly far, facing right.
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 a full directional scan from the green chair to uncover unseen objects and especially locate the red door. Rotate 90° clockwise (from west to north) and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a consistent global map (agent initial origin). Use integer coordinates approximating observed distance/bearing bins; include only observed entities and global cardinal facings where known.
FINAL ANSWER: {
"agent": { "position": [-1, -7], "facing": "north" },
"brown door": { "positi...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -7
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  },
  "plant": {
    "position": [
      1,
      -2
    ]
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -7
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -7
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Only the brown door is in the current FOV; represent it in the agent-centered local frame with +y forward (current facing). Use an integer position consistent with “mid distance” straight ahead.
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": "north"
  }
}

📊 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-F are all located in the upper-right room area beyond the currently observed region (we have not observed the upper connection/doorway area near A/B nor the deeper interior around C/D/E/F), so they represent unexplored regions.
FINAL ANSWER:
{
"unexplored": ["A", "B"...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#......#
#......#E.....#
#......+.AB...#
#......#F.....#
#......#CD....#
###########+###
######......###
######......###
######......###
######.....*###
######......###
######......###
###############

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: F, C, D

🧠 Cognitive Map Metrics

Global:

dir: 0.945

facing: 1

pos: 0.903

overall: 0.950

Global (Full):

dir: 0.433

facing: 0.667

pos: 0.602

overall: 0.567

Local:

dir: 1

facing: 1

pos: 0.877

overall: 0.959

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.366

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: 0.625

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 11

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• brown door: front, mid distance, 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:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
From the green chair, only the brown door is visible when facing this way; the remaining objects and the red door are still unlocated. Continue scanning the room from this central position by rotating 90° clockwise to cover a new sector and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a globally consistent integer-coordinate map from binned observations; fix agent origin at (0,0), propagate relative placements from the brown doorway vantage, and convert object-facing (left/right/forward/backward) into global cardinal facings using the agent’s facing at first report.
F...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -6
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  },
  "plant": {
    "position": [
      2,
      -3
    ]
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -4,
      2
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -6
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -7
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -2,
      -6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: [No objects or doors are currently in the agent’s field of view, 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)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A, B, C, D, E, and F are in the upper rooms beyond doors/walls that have not been entered or directly observed yet, so they lie in unexplored regions.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: E, A, F

🧠 Cognitive Map Metrics

Global:

dir: 0.818

facing: 0.875

pos: 0.866

overall: 0.853

Global (Full):

dir: 0.375

facing: 0.583

pos: 0.578

overall: 0.512

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.366

is_action_fail: False

step: 11

action_counts:

move: 2

rotate: 8

return: 0

observe: 11

term: 0

forced_term: 0

query: 0

information_gain: 0.625

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 12

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe: No objects in field of view..
You have a maximum of 9 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
We’re missing the red door and several objects (shoppingcart, airplane, laptop, backpack). Likely need to re-enter the brown doorway (best multi-room vantage), then look back into room 1 to reposition near truck/scooter and search for the red door. I’ll rotate back to where the brown door is visible...

💬 Assistant Action
Actions: [Rotate(-90), JumpTo(brown door), Rotate(180), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a consistent global (origin-based) integer-coordinate map including only observed entities, with global cardinal facings inferred from the agent orientation at first observation for each object that had a facing.
FINAL ANSWER:
```json
{
"agent": { "position": [-1, -3], "facing": "sout...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -2,
      4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -2,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      2,
      -2
    ]
  },
  "green chair": {
    "position": [
      -1,
      -6
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -7
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -3,
      -6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Need a local (agent-centered) JSON map including only currently observed items in FOV, with approximate integer positions consistent with relative bearings/distances and local facings.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"green chair": {
"position": [0, 3],
"fa...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green chair": {
      "position": [
        0,
        3
      ],
      "facing": "-x"
    },
    "cap": {
      "position": [
        0,
        6
      ],
      "facing": "+y"
    },
    "toilet": {
      "position": [
        3,
        5
      ],
      "facing": "+y"
    },
    "television": {
      "position": [
        4,
        4
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "toilet": {
    "position": [
      2,
      4
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      0,
      6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      4,
      6
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green chair": {
      "position": [
        0,
        3
      ],
      "facing": "-x"
    },
    "cap": {
      "position": [
        0,
        6
      ],
      "facing": "+y"
    },
    "toilet": {
      "position": [
        3,
        5
      ],
      "facing": "+y"
    },
    "television": {
      "position": [
        4,
        4
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: D/E/F lie in the farther, still-unseen part of the top-right room beyond our observed sectors; A/B/C are closer to the already-covered doorway area.
FINAL ANSWER:
{
"unexplored": ["D", "E", "F"]
}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: D, E, F

🎯 Ground Truth: A, E, B

🧠 Cognitive Map Metrics

Global:

dir: 0.836

facing: 1

pos: 0.852

overall: 0.896

Global (Full):

dir: 0.383

facing: 0.667

pos: 0.568

overall: 0.539

Local:

dir: 0.500

facing: 1

pos: 0.841

overall: 0.780

Local (Newly):

(none)

Fog Probe:

precision: 0.333

recall: 0.333

overall: 0.333

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.366

is_action_fail: False

step: 12

action_counts:

move: 3

rotate: 10

return: 0

observe: 12

term: 0

forced_term: 0

query: 0

information_gain: 0.625

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 13

👤 Environment Observation (click to toggle)
You rotated counterclockwise 90°. You jumped to brown door. You rotated clockwise 180°. You observe:
• green chair: front, mid distance, facing left
• cap: front, slightly far, facing forward
• toilet: front-right, slightly far, facing forward
• television: front-right, slightly far, facing backwar...

🤔 Assistant Thinking (click to toggle)
I’m in the brown doorway but currently looking into the same room as the green chair/toilet/TV. To search room 1 for the red door and remaining objects, rotate 180° to look through the doorway into the other room, then observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a single consistent global placement (integer grid) for all observed items relative to the initial origin, using the binned bearings/distances and the doorway jumps; keep only observed objects/door and include facing where given, plus current agent pose.
FINAL ANSWER:
{
"agent": { "pos...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      -3
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -2,
      -3
    ]
  },
  "plant": {
    "position": [
      2,
      -3
    ]
  },
  "scooter": {
    "position": [
      2,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -2,
      -7
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -2,
      -9
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -8
    ],
    "facing": "west"
  },
  "television": {
    "position": [
      -4,
      -8
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Need a local (agent-centered) JSON map with only currently visible objects, assigning integer coordinates consistent with the observed egocentric bearings/distances; include facing axes for objects that have facing.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"truck": {
"pos...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "truck": {
      "position": [
        -3,
        5
      ],
      "facing": "+x"
    },
    "teddybear": {
      "position": [
        -2,
        6
      ],
      "facing": "+x"
    },
    "scooter": {
      "position": [
        2,
        6
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "teddybear": {
    "position": [
      -2,
      6
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -2,
      4
    ],
    "facing": "east"
  },
  "scooter": {
    "position": [
      2,
      6
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "truck": {
      "position": [
        -3,
        5
      ],
      "facing": "+x"
    },
    "teddybear": {
      "position": [
        -2,
        6
      ],
      "facing": "+x"
    },
    "scooter": {
      "position": [
        2,
        6
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Candidates D and E lie beyond the interior doorway into an area we have not observed/entered, while A, B, C, F are in the already-observed region near the current doorway.
FINAL ANSWER:
{
"unexplored": ["D", "E"]
}...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#......#
#......#......#
#......+ED....#
#......#B.....#
#......#FCA...#
###########*###
######......###
######......###
######......###
######......###
######......###
######......###
###############

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: D, E

🎯 Ground Truth: C, B, F

🧠 Cognitive Map Metrics

Global:

dir: 0.600

facing: 0.500

pos: 0.852

overall: 0.651

Global (Full):

dir: 0.275

facing: 0.333

pos: 0.568

overall: 0.392

Local:

dir: 0.667

facing: 1

pos: 0.899

overall: 0.855

Local (Newly):

(none)

Fog Probe:

precision: 0

recall: 0

overall: 0

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.366

is_action_fail: False

step: 13

action_counts:

move: 3

rotate: 11

return: 0

observe: 13

term: 0

forced_term: 0

query: 0

information_gain: 0.625

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 14

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• scooter: front-slight-right, slightly far, facing right
• truck: front-left, slightly far, facing right
• teddybear: front-slight-left, slightly far, facing right
Assume your current facing direction is called "north":
• truck is south, near to teddybear.
...

🤔 Assistant Thinking (click to toggle)
From the brown doorway looking into room 1, only scooter/truck/teddybear are visible; the red door and other objects may be outside this 90° FOV. Rotate 90° to scan a different sector of room 1 from the doorway and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a globally consistent integer-coordinate map (origin at initial agent position), using qualitative bearings/distances to place observed objects and preserve reported object facings in global cardinal directions.
FINAL ANSWER:
```json
{
"agent": { "position": [-1, -2], "facing": "...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -2
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -2
    ]
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -4,
      4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -4,
      2
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      2,
      -1
    ]
  },
  "green chair": {
    "position": [
      -2,
      -5
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -7
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -4,
      -5
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -6,
      -7
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local map from current FOV only; plant is front-left at mid distance, so place it at an approximate integer offset (-1, +3) in the agent-centric frame.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"plant": {
"position": [-1, 3]
}
}
}
```...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "plant": {
      "position": [
        -1,
        3
      ]
    }
  }
}

🎯 Ground Truth

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

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "plant": {
      "position": [
        -1,
        3
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A-F are all located in the upper-right room area beyond the door, which has not been entered/observed yet, so they are in unexplored regions.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#......#
#......#...A..#
#......+......#
#......#C..B..#
#......#DEF...#
###########*###
######......###
######......###
######......###
######......###
######......###
######......###
###############

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: E, D, C

🧠 Cognitive Map Metrics

Global:

dir: 0.782

facing: 1

pos: 0.806

overall: 0.863

Global (Full):

dir: 0.358

facing: 0.667

pos: 0.538

overall: 0.521

Local:

dir: 0

facing: 1

pos: 0.877

overall: 0.626

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.366

is_action_fail: False

step: 14

action_counts:

move: 3

rotate: 12

return: 0

observe: 14

term: 0

forced_term: 0

query: 0

information_gain: 0.625

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 15

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• plant: front-left, mid distance.
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)
Continue sweeping room 1 from the brown doorway to locate the red door and any missing objects; rotate another 90° clockwise to cover the next sector and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Infer a consistent integer-coordinate layout relative to the origin using the binned bearings/distances; anchor the brown door from the origin observation, then place room-2 objects further “in front” of the doorway when facing south, and place truck from the doorway’s opposite-facing observa...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -2,
      4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      2,
      -3
    ]
  },
  "green chair": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -3,
      -8
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -4,
      -7
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -3
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) map from the current FOV; assign integer coordinates consistent with the reported direction bins and distance bins, and include facing for objects that specify it.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"green chair": { "position": [0, 3], "faci...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green chair": {
      "position": [
        0,
        3
      ],
      "facing": "-x"
    },
    "cap": {
      "position": [
        0,
        6
      ],
      "facing": "+y"
    },
    "toilet": {
      "position": [
        4,
        4
      ],
      "facing": "+y"
    },
    "television": {
      "position": [
        5,
        3
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "toilet": {
    "position": [
      2,
      4
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      0,
      6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      4,
      6
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green chair": {
      "position": [
        0,
        3
      ],
      "facing": "-x"
    },
    "cap": {
      "position": [
        0,
        6
      ],
      "facing": "+y"
    },
    "toilet": {
      "position": [
        4,
        4
      ],
      "facing": "+y"
    },
    "television": {
      "position": [
        5,
        3
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Candidate points A-F all lie in areas not directly observed/confirmed by our limited FOV scans, so they are in unexplored/unobserved regions.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: D, F, C

🧠 Cognitive Map Metrics

Global:

dir: 0.800

facing: 0.875

pos: 0.866

overall: 0.847

Global (Full):

dir: 0.367

facing: 0.583

pos: 0.578

overall: 0.509

Local:

dir: 0.700

facing: 1

pos: 0.776

overall: 0.825

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.366

is_action_fail: False

step: 15

action_counts:

move: 3

rotate: 13

return: 0

observe: 15

term: 0

forced_term: 0

query: 0

information_gain: 0.625

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 16

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• green chair: front, mid distance, facing left
• cap: front, slightly far, facing forward
• toilet: front-right, slightly far, facing forward
• television: front-right, slightly far, facing backward
Assume your current facing direction is called "north":
• g...

🤔 Assistant Thinking (click to toggle)
We still haven’t found the red door (likely the connection to the third room) or the remaining objects. From the brown doorway, the view is repeatedly the same; we need a new vantage inside this room. Jump to the toilet (near a different wall) and observe to try to reveal the red door and any unseen...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a single globally-referenced JSON map (origin at initial agent pose). Since no Query() was used, coordinates are inferred consistently from relative observations, jump actions, and distance bins; include only observed items/door and best-estimate cardinal facings.
FINAL ANSWER:
```json
{...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      -7
    ],
    "facing": "south"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -4,
      5
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -4,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      2,
      -2
    ]
  },
  "green chair": {
    "position": [
      -1,
      -6
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -2,
      -9
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -2,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -3,
      -8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map from the current view: cap is front-left at mid distance facing forward; television is front-right at mid distance facing backward. Use integer coordinates consistent with these relations.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"cap": {
...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

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

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A-F cluster in the upper-right room area near the interior wall/doorway region that we have not directly observed from our prior viewpoints; these are likely still unobserved cells to prioritize.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#......#
#......#B.C...#
#......+......#
#......#F.E...#
#......#AD....#
###########+###
######......###
######......###
######......###
######...*..###
######......###
######......###
###############

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: A, F, D

🧠 Cognitive Map Metrics

Global:

dir: 0.691

facing: 1

pos: 0.831

overall: 0.841

Global (Full):

dir: 0.317

facing: 0.667

pos: 0.554

overall: 0.512

Local:

dir: 1

facing: 1

pos: 0.877

overall: 0.959

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.415

is_action_fail: False

step: 16

action_counts:

move: 4

rotate: 13

return: 0

observe: 16

term: 0

forced_term: 0

query: 0

information_gain: 0.643

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 17

👤 Environment Observation (click to toggle)
You jumped to toilet. You observe:
• cap: front-left, mid distance, facing forward
• television: front-right, mid distance, facing backward.
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 toilet, only cap and television are visible; the red door and remaining objects are still missing. Do a directional sweep from this new location: rotate 90° clockwise to expose a new 90° sector and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a single consistent global (origin-at-start) integer-coordinate map for all observed items/doors/agent; approximate coordinates using binned distances/bearings and enforce consistency across multiple observations; include facing only where reported.
FINAL ANSWER:
{
"agent": { "position...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      -6
    ],
    "facing": "west"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  },
  "plant": {
    "position": [
      2,
      -3
    ]
  },
  "scooter": {
    "position": [
      2,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      5
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -6
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -7
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -2,
      -6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      -7
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      -7
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local map for the current FOV only (agent at origin; +y is facing forward). Television is front-left at mid distance; approximate with integer offset [-1, 2]. Its facing is to the agent’s right, i.e., +x.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"television": {
"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "television": {
      "position": [
        -1,
        2
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

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

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "television": {
      "position": [
        -1,
        2
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Most of room2’s far/right and upper areas (candidates A-F, C, D, E) have not been directly observed in our limited 90° FOV sweeps; they likely lie in still-unobserved regions and may also lead toward the unseen connecting door to the next room.
FINAL ANSWER:
```json
{
"unexplored": ["A", "...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#E.....#
#......#.....C#
#......+......#
#......#A.....#
#......#DB.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: 0.745

facing: 1

pos: 0.827

overall: 0.858

Global (Full):

dir: 0.342

facing: 0.667

pos: 0.551

overall: 0.520

Local:

dir: 1

facing: 1

pos: 0.877

overall: 0.959

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.415

is_action_fail: False

step: 17

action_counts:

move: 4

rotate: 14

return: 0

observe: 17

term: 0

forced_term: 0

query: 0

information_gain: 0.643

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 18

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• television: front-left, mid distance, facing right.
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)
With only 3 steps left and current view showing just the television, jumping to the television (a different position in the room) is more likely than another in-place rotation to reveal the missing red door and unseen objects....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Inferred approximate integer coordinates relative to the initial origin from binned egocentric observations across viewpoints; included only observed entities and added facing where reported (mapped right/east, left/west, forward/north, backward/south). Final agent pose is at the television, ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -5,
      -9
    ],
    "facing": "west"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  },
  "plant": {
    "position": [
      1,
      -3
    ]
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -8
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -6
    ],
    "facing": "west"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -5,
      -9
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -5,
      -9
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Current field of view from the television position contains no observed objects or doors, so the local cognitive map has 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: The candidate points A, C, E, F are in upper interior areas not covered by our limited observations (we only sampled near the doorway/objects), and B, D are in the far-right region beyond a door that we never accessed/observed. All labeled candidates lie in currently unobserved space.
FINAL A...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#......#
#......#......#
#......+....DB#
#......#C.....#
#......#FA..E.#
###########+###
######......###
######......###
######......###
######......###
######......###
######.*....###
###############

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: F, C, A

🧠 Cognitive Map Metrics

Global:

dir: 0.927

facing: 0.500

pos: 0.920

overall: 0.783

Global (Full):

dir: 0.425

facing: 0.333

pos: 0.614

overall: 0.457

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.415

is_action_fail: False

step: 18

action_counts:

move: 5

rotate: 14

return: 0

observe: 18

term: 0

forced_term: 0

query: 0

information_gain: 0.643

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 19

👤 Environment Observation (click to toggle)
You jumped to television. You observe: No objects in field of view..
You have a maximum of 2 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
From the television, the current 90° sector is empty; with only two steps left, the best chance to reacquire landmarks (toilet/cap/brown door) and possibly spot the red door is a large rotation. Rotate 180° and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need an integer global map anchored at initial agent pose; use jumps (agent co-locates with objects/door) plus earlier relative bearings/distances to place all observed items consistently, and assign object facing by converting relative facing (when given) into global cardinal directions.
FIN...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -4,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ]
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -4,
      2
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      2,
      -2
    ]
  },
  "green chair": {
    "position": [
      -2,
      -6
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -2,
      -8
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -4,
      -6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -4,
      -9
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -5,
      -9
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -5,
      -9
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a locally consistent map (origin at agent, +y forward) that matches the observed egocentric bearings/distances and the reported near relations.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"cap": { "position": [0, 4], "facing": "+x" },
"green chair": { "position"...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

{
  "toilet": {
    "position": [
      -2,
      2
    ],
    "facing": "south"
  },
  "cap": {
    "position": [
      0,
      4
    ],
    "facing": "south"
  },
  "green chair": {
    "position": [
      -2,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The starred room (current explored area) is the lower-right compartment; all candidate points A-F are in the upper compartments across doors/walls and thus lie in regions not yet observed from the agent’s explored space.
FINAL ANSWER: {
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#..E...#
#......#......#
#......+..C...#
#......#F..A..#
#......#DB....#
###########+###
######......###
######......###
######......###
######......###
######......###
######.*....###
###############

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: F, B, D

🧠 Cognitive Map Metrics

Global:

dir: 0.800

facing: 0.875

pos: 0.872

overall: 0.849

Global (Full):

dir: 0.367

facing: 0.583

pos: 0.581

overall: 0.510

Local:

dir: 1

facing: 1

pos: 0.927

overall: 0.976

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.439

is_action_fail: False

step: 19

action_counts:

move: 5

rotate: 15

return: 0

observe: 19

term: 0

forced_term: 0

query: 0

information_gain: 0.643

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 20

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• green chair: front-left, slightly far, facing forward
• cap: front, mid distance, facing right
• toilet: front-left, mid distance, facing right
Assume your current facing direction is called "north":
• green chair is west, near to cap
• green chair is nort...

🤔 Assistant Thinking (click to toggle)
Only 1 step remains and we still haven’t located the red door or the remaining objects; further mapping isn’t possible within the step budget. Terminate exploration....

💬 Assistant Action
Actions: [Term()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single global map consistent with all observations using the initial origin/frame; approximate integer coordinates consistent with relative bearings/distances and noted near-relations.
FINAL ANSWER:
{
"agent": { "position": [-3, -8], "facing": "east" },
"brown door": { "positi...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -3,
      -8
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      0,
      -3
    ]
  },
  "plant": {
    "position": [
      2,
      -2
    ]
  },
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "teddybear": {
    "position": [
      -3,
      5
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -5,
      5
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      0,
      -6
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      0,
      -8
    ],
    "facing": "south"
  },
  "toilet": {
    "position": [
      -2,
      -6
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -3,
      -8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -5,
      -9
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "scooter": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -7,
      3
    ],
    "facing": "north"
  },
  "green chair": {
    "position": [
      -1,
      -7
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      -11,
      3
    ],
    "facing": "west"
  },
  "cap": {
    "position": [
      -1,
      -9
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -3,
      3
    ],
    "facing": "east"
  },
  "plant": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -7,
      -1
    ],
    "facing": "north"
  },
  "toilet": {
    "position": [
      -3,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      -11,
      1
    ],
    "facing": "east"
  },
  "television": {
    "position": [
      -5,
      -9
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "brown door": {
    "position": [
      -1,
      -3
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -5,
      -9
    ],
    "facing": "east"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.800

facing: 1

pos: 0.765

overall: 0.855

Global (Full):

dir: 0.367

facing: 0.667

pos: 0.510

overall: 0.514

Local:

(none)

Local (Newly):

(none)

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.439

is_action_fail: False

step: 20

action_counts:

move: 5

rotate: 15

return: 0

observe: 19

term: 1

forced_term: 0

query: 0

information_gain: 0.643

reward: -0.100

is_done: True

is_valid_action: True

🔄 FB Turn 21

👤 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)
Start by re-observing from the initial pose (origin, facing north) to compare against the prior baseline for scooter and teddybear....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must output a complete global JSON map with integer grid coordinates for all listed objects/doors plus the agent, using the initial position as origin and cardinal facings.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "north" },
"scooter": { "position": [1, 3], "facing": "e...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.394

facing: 0.375

pos: 0.356

overall: 0.375

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

all_changed_objects: ['airplane', 'laptop', 'teddybear', 'television']

newly_observed_changed (this turn): []

unchanged_objects: ['backpack', 'brown door', 'cap', 'green chair', 'plant', 'red door', 'scooter', 'shoppingcart', 'toilet', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

🧭 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': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: ['scooter']

🔄 FB Turn 22

👤 Environment Observation (click to toggle)
You observe:
• scooter: front-slight-right, mid distance, facing right.
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)
Baseline from this pose had both scooter and teddybear visible; now teddybear is missing, suggesting it moved (or scooter moved blocking, but scooter looks same). Rotate to scan adjacent sector to locate teddybear and detect other changes....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must output a complete global JSON map (agent, all 12 objects, and both doors) using integer coordinates in the initial global frame; include facing for any object with known facing.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "west" },
"scooter": { "position": [1, 3], "fa...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: airplane:

dir: None

pos: 0.877

facing: None

overall: None

Retention: airplane:

dir: None

pos: None

facing: None

overall: None

Unchanged (all):

dir: 0.652

facing: 0.750

pos: 0.781

overall: 0.728

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

all_changed_objects: ['airplane', 'laptop', 'teddybear', 'television']

newly_observed_changed (this turn): ['airplane']

unchanged_objects: ['backpack', 'brown door', 'cap', 'green chair', 'plant', 'red door', 'scooter', 'shoppingcart', 'toilet', 'truck']

pred_changed_keys: ['airplane']

pred_unchanged_keys: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

🧭 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': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['airplane']

newly_observed_unchanged_objects: ['red door', 'truck']

🔄 FB Turn 23

👤 Environment Observation (click to toggle)
You rotated counterclockwise 90°. You observe:
• truck: front-slight-right, mid distance, facing backward
• airplane: front-right, mid distance, facing forward
• red door: front, slightly far, on front wall
Assume your current facing direction is called "north":
• truck is north, near to airplane.
...

🤔 Assistant Thinking (click to toggle)
New observation revealed a red door (previously unseen) plus airplane, and the truck’s pose appears different from the earlier baseline. Best next is to jump to the red door doorway to gain visibility into both connected rooms and quickly locate missing/changed objects (including the teddybear)....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single consistent global map using all observed relative bearings/distances; approximate integer coordinates from distance bins, keeping doors on plausible walls and placing unobserved objects in plausible free locations.
FINAL ANSWER: {
"agent": { "position": [-5, 0], "facing":...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: laptop:

dir: None

pos: None

facing: 0

overall: None

Retention: laptop:

dir: None

pos: None

facing: None

overall: None

Unchanged (all):

dir: 0.697

facing: 0.250

pos: 0.821

overall: 0.589

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

all_changed_objects: ['airplane', 'laptop', 'teddybear', 'television']

newly_observed_changed (this turn): ['laptop']

unchanged_objects: ['backpack', 'brown door', 'cap', 'green chair', 'plant', 'red door', 'scooter', 'shoppingcart', 'toilet', 'truck']

pred_changed_keys: ['laptop']

pred_unchanged_keys: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

🧭 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': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['laptop']

newly_observed_unchanged_objects: ['backpack']

🔄 FB Turn 24

👤 Environment Observation (click to toggle)
You jumped to red door. You observe:
• laptop: front-left, mid distance, facing left
• backpack: front-slight-right, slightly far, facing backward.
You have a maximum of 12 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ...

🤔 Assistant Thinking (click to toggle)
From the red door doorway, rotate to look back into the connected room (room 1) to check whether teddybear/truck/scooter positions/orientations differ from the baseline....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must output a single JSON global map including the agent, all 12 objects, and both doors, using the initial position as origin and assigning consistent integer coordinates with known relative constraints; unseen items get best-guess placements.
FINAL ANSWER: {
"agent": { "position": [-5, ...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.424

facing: 0.125

pos: 0.769

overall: 0.440

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

all_changed_objects: ['airplane', 'laptop', 'teddybear', 'television']

newly_observed_changed (this turn): []

unchanged_objects: ['backpack', 'brown door', 'cap', 'green chair', 'plant', 'red door', 'scooter', 'shoppingcart', 'toilet', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

🧭 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': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

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

🔄 FB Turn 25

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• scooter: front-left, slightly far, facing forward
• truck: front-left, mid distance, facing forward
• airplane: front-slight-left, mid distance, facing backward
• plant: front-slight-right, slightly far
• brown door: front-right, slightly far, on right wal...

🤔 Assistant Thinking (click to toggle)
Recreate the key baseline viewpoint at the brown door (same place, facing into room 1) to compare scooter/truck/teddybear against the earlier observations from that doorway....

💬 Assistant Action
Actions: [JumpTo(brown door), Rotate(-90), Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will output a single consistent global coordinate map (origin at initial agent pose) that includes the agent, all objects, and both doors, using the observations/relations to place items approximately and keep the explicitly stated near/relative relations (e.g., green chair near cap and toi...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.379

facing: 0.500

pos: 0.567

overall: 0.482

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

all_changed_objects: ['airplane', 'laptop', 'teddybear', 'television']

newly_observed_changed (this turn): []

unchanged_objects: ['backpack', 'brown door', 'cap', 'green chair', 'plant', 'red door', 'scooter', 'shoppingcart', 'toilet', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

🧭 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': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 26

👤 Environment Observation (click to toggle)
You jumped to brown door. You rotated counterclockwise 90°. You observe:
• scooter: front-slight-right, slightly far, facing right
• truck: front-left, slightly far, facing right
• airplane: front-slight-left, slightly far, facing left
Assume your current facing direction is called "north":
• truck...

🤔 Assistant Thinking (click to toggle)
To identify which *previously mapped* objects changed, I should compare against the baseline from the exact same vantage/orientation. From the brown door, rotate 180° to face into room 2 (same as the original doorway view) and observe green chair/cap/toilet/television for differences....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a single consistent global grid map (origin at initial agent pose). Use inferred approximate integer coordinates consistent with observed relative relations; include all objects/doors and current agent pose.
FINAL ANSWER:
```json
{
"agent": { "position": [-1, -3], "facing": "south" },...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: television:

dir: None

pos: None

facing: 0

overall: None

Retention: television:

dir: None

pos: None

facing: 0

overall: None

Changed: teddybear:

dir: None

pos: 0.746

facing: None

overall: None

Retention: teddybear:

dir: None

pos: 0.198

facing: None

overall: None

Unchanged (all):

dir: 0.530

facing: 0.625

pos: 0.740

overall: 0.632

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

all_changed_objects: ['airplane', 'laptop', 'teddybear', 'television']

newly_observed_changed (this turn): ['teddybear', 'television']

unchanged_objects: ['backpack', 'brown door', 'cap', 'green chair', 'plant', 'red door', 'scooter', 'shoppingcart', 'toilet', 'truck']

pred_changed_keys: ['teddybear', 'television']

pred_unchanged_keys: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

🧭 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': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['television', 'teddybear']

newly_observed_unchanged_objects: ['cap', 'toilet', 'green chair']

🔄 FB Turn 27

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• green chair: front, mid distance, facing left
• cap: front, slightly far, facing forward
• teddybear: front-slight-right, mid distance, facing left
• toilet: front-right, slightly far, facing forward
• television: front-right, slightly far, facing right
As...

🤔 Assistant Thinking (click to toggle)
We’ve confirmed at least one clear change: television’s facing is different (was facing backward; now facing right) from the same brown-door viewpoint. Also teddybear is now in this room whereas it was previously in room 1, so it likely changed position. We still need to identify 2 more changed obje...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a globally consistent integer-coordinate map (origin at initial agent pose), placing all observed items/doors with approximate positions consistent with relative bearings/distances; include unobserved shoppingcart with a plausible placeholder location.
FINAL ANSWER:
{
"agent": { ...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.667

facing: 1

pos: 0.660

overall: 0.775

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

all_changed_objects: ['airplane', 'laptop', 'teddybear', 'television']

newly_observed_changed (this turn): []

unchanged_objects: ['backpack', 'brown door', 'cap', 'green chair', 'plant', 'red door', 'scooter', 'shoppingcart', 'toilet', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

🧭 False Belief Info

step: 7

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': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 28

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• scooter: front-slight-right, slightly far, facing right
• truck: front-left, slightly far, facing right
• airplane: front-slight-left, slightly far, facing left
Assume your current facing direction is called "north":
• truck is west, near to airplane.
You ...

🤔 Assistant Thinking (click to toggle)
We’ve identified likely changes: television orientation (seen from the same brown-door viewpoint) and teddybear position (now in room 2 instead of room 1). We also now see airplane from the same brown-door/room1 viewpoint where it was previously absent, implying airplane position changed. To find th...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must provide a complete global JSON map (agent, all objects, both doors) with integer coordinates relative to the initial origin and cardinal facings where applicable; since exact coordinates were never queried, I’m outputting a single consistent layout that fits the observed qualitative re...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.591

facing: 0.750

pos: 0.789

overall: 0.710

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

all_changed_objects: ['airplane', 'laptop', 'teddybear', 'television']

newly_observed_changed (this turn): []

unchanged_objects: ['backpack', 'brown door', 'cap', 'green chair', 'plant', 'red door', 'scooter', 'shoppingcart', 'toilet', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

🧭 False Belief Info

step: 8

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': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 29

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• plant: front-left, mid distance.
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)
We have 3 strong change candidates already: (1) television orientation differs from the same brown-door viewpoint, (2) teddybear appears in room 2 whereas it was in room 1 before ⇒ position change, (3) airplane now appears in the room-1 view from brown door where teddybear used to be ⇒ airplane posi...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single consistent global map (origin at initial agent pose). Use rough integer placements consistent with observed bearings/distances across viewpoints; include all objects/doors and agent with cardinal facings where known/approximated.
FINAL ANSWER:
```json
{
"agent": { "position"...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.758

facing: 0.750

pos: 0.824

overall: 0.777

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

all_changed_objects: ['airplane', 'laptop', 'teddybear', 'television']

newly_observed_changed (this turn): []

unchanged_objects: ['backpack', 'brown door', 'cap', 'green chair', 'plant', 'red door', 'scooter', 'shoppingcart', 'toilet', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

🧭 False Belief Info

step: 9

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': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 30

👤 Environment Observation (click to toggle)
Cannot jump to 'green chair': object not visible. Subsequent actions are skipped due to failure, instead an observe is executed: You observe:
• plant: front-left, mid distance.
You have a maximum of 6 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
F...

🤔 Assistant Thinking (click to toggle)
My last JumpTo failed because green chair wasn’t in view. I’m at the brown door, currently facing the sector where only the plant is visible. Rotate 90° clockwise to face into room 2 (where green chair/cap/toilet/television/teddybear should be visible), observe to reacquire green chair, then I can j...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Best-effort global map using the partial relative observations; several coordinates/orientations are approximate due to missing queries and limited prior coverage.
FINAL ANSWER:
{
"agent": { "position": [-1, -3], "facing": "north" },
"scooter": { "position": [2, 3], "facing": "east" },
...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.500

facing: 0.375

pos: 0.722

overall: 0.532

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

all_changed_objects: ['airplane', 'laptop', 'teddybear', 'television']

newly_observed_changed (this turn): []

unchanged_objects: ['backpack', 'brown door', 'cap', 'green chair', 'plant', 'red door', 'scooter', 'shoppingcart', 'toilet', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'backpack', 'brown door', 'cap', 'green chair', 'laptop', 'plant', 'red door', 'scooter', 'shoppingcart', 'teddybear', 'television', 'toilet', 'truck']

🧭 False Belief Info

step: 10

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': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 31

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• green chair: front, mid distance, facing left
• cap: front, slightly far, facing forward
• teddybear: front-slight-right, mid distance, facing left
• toilet: front-right, slightly far, facing forward
• television: front-right, slightly far, facing right
Ass...

🤔 Assistant Thinking (click to toggle)
From comparisons to the earlier exploration views: (1) teddybear is no longer in room 1 and is now visible in room 2 near the green chair → position change; (2) television’s facing from the brown-door room-2 viewpoint differs (was facing backward, now facing right) → orientation change; (3) airplane...

💬 Assistant Action
Actions: [Term(changes="teddybear: position, television: orientation, airplane: position, truck: orientation")]

📈 Turn Metrics

reward: 0.750

is_done: True

is_valid_action: True

success: 0.750

🧭 False Belief Info

step: 11

correctly_identified_changes: 0.750

f1_overall: 0.750

f1_position: 1

f1_facing: 0.500

action_cost: 10

action_cost_after_seen: 4

ground_truth_changes: [{'name': 'laptop', 'ori': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'teddybear', 'pos': True}]

reported_changes: [{'name': 'teddybear', 'pos': True}, {'name': 'television', 'ori': True}, {'name': 'airplane', 'pos': True}, {'name': 'truck', 'ori': True}]

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

📊 Task: DirectionEvaluationTask

Question 1 (ID: 583ff194ce53a312)

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

🤔 Assistant Answer (click to toggle)
THINK:
I don’t have direct observations of the shoppingcart’s location, so I must provide a best-guess directional relation.
FINAL ANSWER:
west, mid distance...

✅ Evaluation Results

id: 583ff194ce53a312

question: You return to your starting position and face north. From a Top-Down map, describe where shoppingcart is relative to scooter. 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: default

Evaluation state — Q1: DirectionEvaluationTask

Question 2 (ID: 47fca34d599857c1)

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

🤔 Assistant Answer (click to toggle)
THINK:
Truck is in the room north of the brown door (room 1) and was observed front-left from the doorway, while green chair is in the room south of the brown door (room 2) and was observed straight ahead from the doorway, so truck is north-west of green chair. Combined doorway-to-object distances p...

✅ Evaluation Results

id: 47fca34d599857c1

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

action: None

answer: north, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 3 (ID: 108ff9b4dbbe7799)

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

🤔 Assistant Answer (click to toggle)
THINK:
Shoppingcart was never observed during exploration, so its position relative to the truck cannot be determined from the constructed map.
FINAL ANSWER:
unknown, unknown...

✅ Evaluation Results

id: 108ff9b4dbbe7799

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

action: None

answer: north west, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

📊 Task: PovEvaluationTask

Question 1 (ID: 77b726d65cc6b33c)

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

🤔 Assistant Answer (click to toggle)
THINK:
From the last observed proximity relations: green chair is north (near) of toilet and west (near) of cap, so cap is northeast of toilet. Facing the toilet’s direction (right/east), northeast becomes front-left; distance likely within (2,4] given two near offsets.
FINAL ANSWER:
front-left, mid...

✅ Evaluation Results

id: 77b726d65cc6b33c

question: Now you jump to toilet's direction, facing its direction. Describe where cap 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: default

Question 2 (ID: 25bf253d96fdcb35)

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

🤔 Assistant Answer (click to toggle)
THINK:
We did not observe backpack or laptop during exploration, so the only plausible relation is that laptop is likely close to backpack; report it as directly ahead and near.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 25bf253d96fdcb35

question: Now you jump to backpack's direction, facing its direction. Describe where laptop 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

evaluation_mode: default

Question 3 (ID: 49542b54da7d1812)

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

🤔 Assistant Answer (click to toggle)
THINK:
After jumping to the television and facing the television’s own facing direction, the toilet falls outside the ±45° ego FOV cone but remains at the same mid-range distance.
FINAL ANSWER:
beyond-fov, mid distance...

✅ Evaluation Results

id: 49542b54da7d1812

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

action: None

answer: front-right, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: f90bc1e2211507f1)

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

🤔 Assistant Answer (click to toggle)
THINK:
Scooter appears “facing forward” now, but earlier it consistently faced “right”, so my current “forward” must align with world-east. Since I’m facing the direction of the object I jumped to, that object must also face east (i.e., had been seen “facing right”). From this east-facing spot, scoo...

✅ Evaluation Results

id: f90bc1e2211507f1

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

action: None

answer:

answer: truck

final_pos: [9, 11]

final_ori: [1, 0]

final_observation: [{'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'plant', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

Question 2 (ID: 52fa8addbc3250f7)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that shoppingcart is front-left, slightly far, facing left; laptop is front-right, slightly far, facing left; red door is front-slight-right, slightly far, on front wall.
Which object are you standing at?
Answer format: <object_...

🤔 Assistant Answer (click to toggle)
THINK:
The viewpoint is from an object whose facing direction aligns with the current “north” (forward). From this spot, shoppingcart and laptop are both ahead (left/right split) and the red door is ahead on the front wall, matching the likely central vantage of the airplane in the third room.
FINAL...

✅ Evaluation Results

id: 52fa8addbc3250f7

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

action: None

answer:

answer: backpack

final_pos: [1, 11]

final_ori: [1, 0]

final_observation: [{'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'laptop', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'red door', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 2

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: default

Question 3 (ID: 0164361f1d8a8c03)

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

🤔 Assistant Answer (click to toggle)
THINK:
If I’m at the television and face its direction (north), the brown door would be on the front (north) wall and appear front-right, and the toilet (which faces south) would appear front-right facing backward.
FINAL ANSWER:
television...

✅ Evaluation Results

id: 0164361f1d8a8c03

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

action: None

answer:

answer: television

final_pos: [7, 1]

final_ori: [0, 1]

final_observation: [{'name': 'toilet', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'brown door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 3

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 63c750790aa08aed)

❓ 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, slightly far.
3. Jump to the object at front-left, mid distance.

After executing the actions, what is the ego relation of backpack relative to you?

An...

🤔 Assistant Answer (click to toggle)
THINK:
Backpack was never observed during exploration, so its position cannot be localized from the available map; relative to the final pose it must be treated as outside the current FOV with unknown exact range. I will answer as beyond-FOV at a far distance bin.
FINAL ANSWER:
beyond-fov, far...

✅ Evaluation Results

id: 63c750790aa08aed

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, slightly far. 3. Jump to the object at front-left, mid distance. After executing the actions, what is the ego relation of backpack relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Question 2 (ID: 6412baabea12ca22)

❓ 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, slightly far.
3. Jump to the object at front-slight-right, slightly far.
4. Rotate(-180)

After executing the actions, what is the ego relation of lapto...

🤔 Assistant Answer (click to toggle)
THINK:
We never observed or localized the laptop during exploration, so its position cannot be determined from the available map; thus only an indeterminate/out-of-view relation can be given.
FINAL ANSWER:
beyond-fov, very far...

✅ Evaluation Results

id: 6412baabea12ca22

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, slightly far. 3. Jump to the object at front-slight-right, slightly far. 4. Rotate(-180) After executing the actions, what is the ego relation of laptop relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Question 3 (ID: ea1bd9387f8ac037)

❓ 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-slight-right, mid distance.
3. Jump to the object at front, mid distance.
4. Rotate(-180)

After executing the actions, what is the ego relation of brow...

🤔 Assistant Answer (click to toggle)
THINK:
After the sequence, I’m at the green chair facing north; the brown door lies north of the green chair at about the same mid-range distance previously observed.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: ea1bd9387f8ac037

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-slight-right, mid distance. 3. Jump to the object at front, mid distance. 4. Rotate(-180) After executing the actions, what is the ego relation of brown door relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 6c52792d9caa463c)

❓ 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:
scooter is at front-left, slightly far, facing forward; plant is at front-slight-right, slightly far; brown door is at front-r...

🤔 Assistant Answer (click to toggle)
THINK:
To get brown door on the right wall while also seeing scooter front-left and plant front-slight-right, the agent likely ends up at the truck and faces east; since JumpTo cannot be first, rotate to bring the truck into FOV, jump, then rotate to the final facing.
FINAL ANSWER:
Rotate(-90), Jump...

✅ Evaluation Results

id: 6c52792d9caa463c

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: scooter is at front-left, slightly far, facing forward; plant is at front-slight-right, slightly far; brown door is at front-right, slightly far, 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: [7, 10]

final_ori: [1, 0]

room_id: [2, 1]

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', -90], ['jumpto', 'initial_pos'], ['rotate', 180]]

final_observation: [{'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'plant', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'brown door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on right wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [9.0, 11.0]

final_ori: [1, 0]

visible_match: False

score: 0

evaluation_mode: default

Question 2 (ID: 3732a26a9f623bf3)

❓ 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:
plant is at front-slight-right, slightly far; scooter is at front-left, slightly far, facing forward; truck is at front-left, ...

🤔 Assistant Answer (click to toggle)
THINK:
From the start (facing north), rotate to face south so the brown door is in view, jump to it, then rotate to face east to match the final observed orientations.
FINAL ANSWER:
Rotate(180), JumpTo(brown door), Rotate(-90)...

✅ Evaluation Results

id: 3732a26a9f623bf3

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: plant is at front-slight-right, slightly far; scooter is at front-left, slightly far, facing forward; truck is at front-left, 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: [7, 10]

final_ori: [1, 0]

room_id: [2, 1]

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', -90], ['jumpto', 'initial_pos'], ['rotate', 180]]

final_observation: [{'name': 'plant', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [11.0, 7.0]

final_ori: [1, 0]

visible_match: False

score: 0

evaluation_mode: default

Question 3 (ID: ea10b24ed30f3b20)

❓ 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:
toilet is at front-right, mid distance, facing left; television is at front, mid distance, facing right

What action sequence ...

🤔 Assistant Answer (click to toggle)
THINK:
Use the brown door as a reachable waypoint from the start, then move to the cap and face back toward the television/toilet side.
FINAL ANSWER:
Rotate(180), JumpTo(brown door), Rotate(180), JumpTo(cap), Rotate(180)...

✅ Evaluation Results

id: ea10b24ed30f3b20

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: toilet is at front-right, mid distance, facing left; television is at front, mid distance, facing right 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: [11, 1]

final_ori: [-1, 0]

room_id: 3

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'brown door'], ['jumpto', 'cap'], ['rotate', 90]]

final_observation: [{'name': 'toilet', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing left'}, {'name': 'television', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

error: target_not_visible

score: 0

evaluation_mode: default

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 8af89065720ad8db)

❓ 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: shoppingcart, scooter, laptop.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Only scooter could be localized from the observations; shoppingcart and laptop were never observed, so their exact coordinates can’t be determined from the provided data. I will output the inferred scooter location and placeholders for the others.
FINAL ANSWER:
(0, 0); (1, 3); (0, 0)...

✅ Evaluation Results

id: 8af89065720ad8db

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

action: None

answer: [[-7, 3], [1, 3], [-7, -1]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.359

threshold: 0.900

score: 0.359

evaluation_mode: default

Question 2 (ID: f662c4ad95bb2232)

❓ 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: truck, green chair, teddybear, scooter, plant.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Infer integer coordinates by choosing a consistent set satisfying all observed egocentric bearings, distance bins, doorway-based multi-room views, and the given near-relations (truck near/south of teddybear). Use the start as (0,0), north as +y.
FINAL ANSWER:
(-4, 2); (-1, -6); (-3, 3); (1, 3...

✅ Evaluation Results

id: f662c4ad95bb2232

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

action: None

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

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.784

threshold: 0.900

score: 0.784

evaluation_mode: default

Question 3 (ID: a8bd9bc1b10b893e)

❓ 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: truck, shoppingcart, plant.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
I don’t have exact coordinates (no Query was used and shoppingcart was never observed), so I must provide my best estimate from the partial qualitative observations.
FINAL ANSWER:
(-4, 3); (-6, -1); (2, -2)...

✅ Evaluation Results

id: a8bd9bc1b10b893e

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

action: None

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

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.452

threshold: 0.900

score: 0.452

evaluation_mode: default

📊 Task: RotEvaluationTask

Question 1 (ID: 8d54e45588e4d433)

❓ 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: green chair, laptop, shoppingcart.
L...

🤔 Assistant Answer (click to toggle)
THINK:
We never observed laptop or shoppingcart during exploration, so their bearings from the start can’t be derived from the provided data. Given the requirement to output an ordered list anyway, I will provide a best-guess ordering assuming a typical left-to-right spread (shoppingcart to the left...

✅ Evaluation Results

id: 8d54e45588e4d433

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: green chair, laptop, shoppingcart. 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: ['shoppingcart', 'laptop', 'green chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Question 2 (ID: d79c067009e69829)

❓ 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: green chair, plant, scooter, truck.
...

🤔 Assistant Answer (click to toggle)
THINK:
Infer approximate bearings from the start: truck is to the left/northwest, green chair to the southwest (beyond the brown door), plant to the southeast, scooter slightly to the right of north; order them by when each aligns with the forward direction during a CCW sweep.
FINAL ANSWER:
truck, g...

✅ Evaluation Results

id: d79c067009e69829

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: green chair, plant, scooter, truck. 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: ['truck', 'green chair', 'plant', 'scooter']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 6888c41ba1414d8a)

❓ 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: green chair, plant, truck.
List them...

🤔 Assistant Answer (click to toggle)
THINK:
Determine each object’s approximate bearing from the start: truck is in the northwest sector, green chair in the southwest sector, plant in the southeast sector. During a counterclockwise rotation from north, objects appear ahead in descending azimuth order: NW → SW → SE.
FINAL ANSWER:
truck,...

✅ Evaluation Results

id: 6888c41ba1414d8a

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: green chair, plant, truck. 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: ['truck', 'green chair', 'plant']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: RotDualEvaluationTask

Question 1 (ID: 89475accf17d4665)

❓ 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:
shoppingcart, laptop, green ...

🤔 Assistant Answer (click to toggle)
THINK:
Green chair was located generally to the southwest of the start (via the brown door seen when facing south). In a 360° turn from facing north, encountering green chair after two other objects fits sweeping from north toward west then southwest, i.e., rotating counterclockwise.
FINAL ANSWER:
c...

✅ Evaluation Results

id: 89475accf17d4665

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: shoppingcart, laptop, green chair 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: 058612f0c21d8b04)

❓ 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:
truck, green chair, plant, s...

🤔 Assistant Answer (click to toggle)
THINK:
From the start facing north, scooter was already in the initial forward FOV (near north), while truck was not—so truck must lie to the left side (westward) beyond the initial ±45° FOV. During a full rotation, encountering truck before scooter implies the view swept leftward first, i.e., count...

✅ Evaluation Results

id: 058612f0c21d8b04

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: truck, green chair, plant, scooter 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: e44020cfb70d49b0)

❓ 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:
truck, green chair, plant

B...

🤔 Assistant Answer (click to toggle)
THINK:
From the start, plant is to the southeast (bearing ≈ +135° = -225°), green chair is roughly south (-180°), and truck is to the northwest (small negative bearing). Seeing them in the order truck → green chair → plant matches decreasing bearings from 0°, i.e., a counterclockwise rotation.
FINAL...

✅ Evaluation Results

id: e44020cfb70d49b0

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: truck, green chair, plant 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: 124fa9437e2c6a92)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (-5, 2) and face west.
What is the egocentric relation of airplane?

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

🤔 Assistant Answer (click to toggle)
THINK:
The airplane’s position is outside the 90° field of view from (-5, 2) while facing west, and its distance falls in the 8–16 bin.
FINAL ANSWER:
beyond-fov, far...

✅ Evaluation Results

id: 124fa9437e2c6a92

question: Treat the red door as the new 'origin' (0, 0). You move to (-5, 2) and face west. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, near

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Question 2 (ID: 2628716be474f0bc)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (-4, 3) and face east.
What is the egocentric relation of shoppingcart?

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

🤔 Assistant Answer (click to toggle)
THINK:
Shoppingcart was never observed or localized during exploration, so its position relative to the new origin and current pose cannot be determined from the provided information.
FINAL ANSWER:
beyond-fov, unknown...

✅ Evaluation Results

id: 2628716be474f0bc

question: Treat the red door as the new 'origin' (0, 0). You move to (-4, 3) and face east. What is the egocentric relation of shoppingcart? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Question 3 (ID: 74c4c67f29139097)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (1, 2) and face south.
What is the egocentric relation of plant?

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

🤔 Assistant Answer (click to toggle)
THINK:
Plant was inferred at (1, -3). From agent at (1, 2) facing south, plant is straight ahead with distance 5 → slightly far.
FINAL ANSWER:
front, slightly far...

✅ Evaluation Results

id: 74c4c67f29139097

question: Still treat your initial position as origin (0, 0) You move to (1, 2) and face south. What is the egocentric relation of plant? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, mid distance

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 4433d7003fa00dbc)

❓ Evaluation Question (click to toggle)
You move to a new location and face west.
You observe: airplane is at front-right, near, facing forward; backpack is at front-left, near, facing backward
Treat the red door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

Answer format: (x, y)
Example: (2, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
With no explicit relative position given between my new location and the red door, the only well-defined interpretation is that I am at the red door, so my coordinate in the red-door-origin frame is the origin.
FINAL ANSWER:
(0, 0)...

✅ Evaluation Results

id: 4433d7003fa00dbc

question: You move to a new location and face west. You observe: airplane is at front-right, near, facing forward; backpack is at front-left, near, facing backward Treat the red door as the new 'origin' (0, 0). What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-5, 2]

final_pos: [2, 12]

final_ori: [-1, 0]

room_id: 2

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

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

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.368

raw_score: 0.368

score: 0.368

evaluation_mode: default

Question 2 (ID: 439fe7bf768dde78)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: shoppingcart is at front, near, facing left; red door is at front-right, slightly far, on front wall
Treat the red door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

Answer format: (x, y)
Example: (2, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Facing east means forward is +x and right is -y. “Front-right, slightly far” is consistent with the red door being at about (+4, -4) from me, so if the red door is (0,0), my coordinate is the negative of that offset.
FINAL ANSWER:
(-4, 4)...

✅ Evaluation Results

id: 439fe7bf768dde78

question: You move to a new location and face east. You observe: shoppingcart is at front, near, facing left; red door is at front-right, slightly far, on front wall Treat the red door as the new 'origin' (0, 0). What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-4, 3]

final_pos: [3, 13]

final_ori: [1, 0]

room_id: 2

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

final_observation: [{'name': 'shoppingcart', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'red door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.819

raw_score: 0.819

score: 0.819

evaluation_mode: default

Question 3 (ID: 55cf256e6a550f6e)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: plant is at front, mid distance; brown door is at front-slight-right, slightly far, on front 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)
...

🤔 Assistant Answer (click to toggle)
THINK:
From the initial origin view, the only integer placement consistent with plant being front-left mid (facing south) is plant at (1, -2) and brown door at (-1, -3). For the new observation (facing south), plant is straight ahead at mid distance, so my x must equal 1 and y must be 2 (4 units nor...

✅ Evaluation Results

id: 55cf256e6a550f6e

question: You move to a new location and face south. You observe: plant is at front, mid distance; brown door is at front-slight-right, slightly far, on front 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: [1, 2]

final_pos: [13, 12]

final_ori: [0, -1]

room_id: 1

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

final_observation: [{'name': 'plant', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'brown door', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: default

📊 Task: DirectionEvaluationTask

Question 1 (ID: 583ff194ce53a312)

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

🤔 Assistant Answer (click to toggle)
THINK:
I will build a single global coordinate assignment (integer grid) consistent with the observed egocentric bearings/distances and key proximity notes, anchored at the initial agent pose (0,0) facing north. Shoppingcart was never observed, so its relative position to scooter cannot be determine...

✅ Evaluation Results

id: 583ff194ce53a312

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

action: None

answer: west, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: 47fca34d599857c1)

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

🤔 Assistant Answer (click to toggle)
THINK:
I need integer coordinates for only the objects I observed, in a single global frame where my initial position is (0,0) and initial facing is north. Since I never used Query(), exact coordinates aren’t directly provided, so I construct one consistent integer layout that matches the observed e...

✅ Evaluation Results

id: 47fca34d599857c1

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

action: None

answer: north, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 108ff9b4dbbe7799)

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

🤔 Assistant Answer (click to toggle)
THINK:
We must build a global (start-anchored) cognitive map using only objects/doors that were actually observed. Exact coordinates were never directly given, so I assign a single consistent integer layout that matches the observed relative relations as closely as possible. Shoppingcart was never o...

✅ Evaluation Results

id: 108ff9b4dbbe7799

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

action: None

answer: north west, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: 77b726d65cc6b33c)

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

🤔 Assistant Answer (click to toggle)
THINK:
I must include only objects that were observed and place them on an integer global grid anchored at the agent’s initial position (0,0), facing north. Since no Query() actions were used, exact coordinates are underdetermined; I’ll provide one consistent integer layout that satisfies the observ...

✅ Evaluation Results

id: 77b726d65cc6b33c

question: Now you jump to toilet's direction, facing its direction. Describe where cap 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 2 (ID: 25bf253d96fdcb35)

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

🤔 Assistant Answer (click to toggle)
THINK:
I never observed (saw) backpack or laptop, and I never used Query(), so I can only place coordinates for the objects that were actually observed, using a single consistent global frame (initial position = [0,0], initial facing = north).
For the final question, since laptop was never observe...

✅ Evaluation Results

id: 25bf253d96fdcb35

question: Now you jump to backpack's direction, facing its direction. Describe where laptop 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

evaluation_mode: prompt_cogmap

Question 3 (ID: 49542b54da7d1812)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll place all **observed** items on an integer grid consistent with the repeated doorway/room observations (approximate but self-consistent), keep the agent’s initial position as (0,0) facing north, and record each object’s intrinsic facing in global cardinal terms by converting from the age...

✅ Evaluation Results

id: 49542b54da7d1812

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

action: None

answer: front-right, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: f90bc1e2211507f1)

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

🤔 Assistant Answer (click to toggle)
THINK:
Build a single consistent global placement (integer grid) that matches the observed relative bearings/distances as closely as possible. For the final riddle, the only observed object whose facing plausibly makes scooter appear front-left and plant front-right at slightly-far range is the truc...

✅ Evaluation Results

id: f90bc1e2211507f1

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

action: None

answer:

answer: truck

final_pos: [9, 11]

final_ori: [1, 0]

final_observation: [{'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'plant', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 52fa8addbc3250f7)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that shoppingcart is front-left, slightly far, facing left; laptop is front-right, slightly far, facing left; red door is front-slight-right, slightly far, on front wall.
Which object are you standing at?
Answer format: <object_...

🤔 Assistant Answer (click to toggle)
THINK:
I need to output a global JSON cognitive map with integer coordinates (origin at my initial position) for only the objects/doors I actually observed: scooter, teddybear, truck, plant, brown door, green chair, cap, toilet, television, plus the agent. Since no Query() actions were used, exact c...

✅ Evaluation Results

id: 52fa8addbc3250f7

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

action: None

answer:

answer: backpack

final_pos: [1, 11]

final_ori: [1, 0]

final_observation: [{'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'laptop', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'red door', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 2

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: prompt_cogmap

Question 3 (ID: 0164361f1d8a8c03)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that toilet is front-right, mid distance, facing backward; brown door is front-right, 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 build a single consistent global grid (origin at initial agent position). Since no Query() was used, exact coordinates weren’t given; I assign integer coordinates that satisfy all observed relative bearings/distances and the given near-relations. Then, for the question, the only observed...

✅ Evaluation Results

id: 0164361f1d8a8c03

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

action: None

answer:

answer: television

final_pos: [7, 1]

final_ori: [0, 1]

final_observation: [{'name': 'toilet', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'brown door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 3

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 63c750790aa08aed)

❓ 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, slightly far.
3. Jump to the object at front-left, mid distance.

After executing the actions, what is the ego relation of backpack relative to you?

An...

🤔 Assistant Answer (click to toggle)
THINK:
I will place the observed objects on an integer grid consistent with the egocentric bearings/distances we saw (approximate, since no Query() was used). Backpack was never observed, so its position is unknown; therefore its ego relation after the given action sequence cannot be determined from...

✅ Evaluation Results

id: 63c750790aa08aed

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, slightly far. 3. Jump to the object at front-left, mid distance. After executing the actions, what is the ego relation of backpack relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: 6412baabea12ca22)

❓ 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, slightly far.
3. Jump to the object at front-slight-right, slightly far.
4. Rotate(-180)

After executing the actions, what is the ego relation of lapto...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll place all observed items on a single consistent integer grid anchored at the start (0,0), using the binned bearings/distances and the noted near-relations to keep relative geometry consistent. The laptop was never observed, so its final egocentric relation cannot be determined.
FINAL ANS...

✅ Evaluation Results

id: 6412baabea12ca22

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, slightly far. 3. Jump to the object at front-slight-right, slightly far. 4. Rotate(-180) After executing the actions, what is the ego relation of laptop relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 3 (ID: ea1bd9387f8ac037)

❓ 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-slight-right, mid distance.
3. Jump to the object at front, mid distance.
4. Rotate(-180)

After executing the actions, what is the ego relation of brow...

🤔 Assistant Answer (click to toggle)
THINK:
I need a global JSON map (origin at my start [0,0], +y = north) containing only objects I observed: scooter, teddybear, plant, brown door, green chair, cap, toilet, television, truck, plus the agent. Since only binned egocentric relations were provided, I assign a single consistent set of int...

✅ Evaluation Results

id: ea1bd9387f8ac037

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-slight-right, mid distance. 3. Jump to the object at front, mid distance. 4. Rotate(-180) After executing the actions, what is the ego relation of brown door relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 6c52792d9caa463c)

❓ 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:
scooter is at front-left, slightly far, facing forward; plant is at front-slight-right, slightly far; brown door is at front-r...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll build a global map using the initial position as (0,0) with +y = north, assigning integer coordinates consistent with the observed relative directions/distances (approximate but coherent). Then, for the action-only sequence question, I’ll choose a valid series of Rotates/Jumps from the s...

✅ Evaluation Results

id: 6c52792d9caa463c

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: scooter is at front-left, slightly far, facing forward; plant is at front-slight-right, slightly far; brown door is at front-right, slightly far, 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: [7, 10]

final_ori: [1, 0]

room_id: [2, 1]

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', -90], ['jumpto', 'initial_pos'], ['rotate', 180]]

final_observation: [{'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'plant', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'brown door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on right wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [9.0, 11.0]

final_ori: [1, 0]

visible_match: False

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: 3732a26a9f623bf3)

❓ 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:
plant is at front-slight-right, slightly far; scooter is at front-left, slightly far, facing forward; truck is at front-left, ...

🤔 Assistant Answer (click to toggle)
THINK:
For the global cognitive map, I assign integer coordinates consistent with all egocentric/binned observations and the doorway constraint (brown door connects two rooms). Object “facing” in observations is relative to the agent at that time, so I convert to global cardinal directions using the...

✅ Evaluation Results

id: 3732a26a9f623bf3

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: plant is at front-slight-right, slightly far; scooter is at front-left, slightly far, facing forward; truck is at front-left, 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: [7, 10]

final_ori: [1, 0]

room_id: [2, 1]

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', -90], ['jumpto', 'initial_pos'], ['rotate', 180]]

final_observation: [{'name': 'plant', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [11.0, 7.0]

final_ori: [1, 0]

visible_match: False

score: 0

evaluation_mode: prompt_cogmap

Question 3 (ID: ea10b24ed30f3b20)

❓ 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:
toilet is at front-right, mid distance, facing left; television is at front, mid distance, facing right

What action sequence ...

🤔 Assistant Answer (click to toggle)
THINK:
For the cognitive map, I can only include objects I observed; since I never used Query(), exact coordinates weren’t given, so I place a single plausible integer-coordinate layout consistent with the qualitative bearings/distances seen from the start and from the brown door area (approximate b...

✅ Evaluation Results

id: ea10b24ed30f3b20

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: toilet is at front-right, mid distance, facing left; television is at front, mid distance, facing right 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: [11, 1]

final_ori: [-1, 0]

room_id: 3

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'brown door'], ['jumpto', 'cap'], ['rotate', 90]]

final_observation: [{'name': 'toilet', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing left'}, {'name': 'television', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [11.0, 1.0]

final_ori: [-1, 0]

visible_match: True

score: 1

evaluation_mode: prompt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 8af89065720ad8db)

❓ 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: shoppingcart, scooter, laptop.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent global (allocentric) map using only observed items, with the start as (0,0) and north as +y. Because shoppingcart and laptop were never observed, their coordinates cannot be determined from the collected observations.
FINAL ANSWER:
<cogmap>
{
"agent": { "positi...

✅ Evaluation Results

id: 8af89065720ad8db

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

action: None

answer: [[-7, 3], [1, 3], [-7, -1]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

error: mismatched_coordinate_count

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: f662c4ad95bb2232)

❓ 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: truck, green chair, teddybear, scooter, plant.
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 can’t be uniquely recovered from only binned bearings/distances. I’m outputting one integer-coordinate map that is consistent with all observations qualitatively (relative left/right/front relations, approximate distance bins, and the truck–teddybea...

✅ Evaluation Results

id: f662c4ad95bb2232

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

action: None

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

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.884

threshold: 0.900

score: 0.884

evaluation_mode: prompt_cogmap

Question 3 (ID: a8bd9bc1b10b893e)

❓ 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: truck, shoppingcart, plant.
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’ll produce a single consistent integer-grid map that matches the observed relative bearings/distances and the stated near-relations. Unobserved items (e.g., shoppingcart) must not be placed in the map, and its coordinate ans...

✅ Evaluation Results

id: a8bd9bc1b10b893e

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

action: None

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

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

error: mismatched_coordinate_count

score: 0

evaluation_mode: prompt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 8d54e45588e4d433)

❓ 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: green chair, laptop, shoppingcart.
L...

🤔 Assistant Answer (click to toggle)
THINK:
I can only place objects that were actually observed. Exact coordinates were never directly queried, so the global grid below is a consistent integer approximation that respects the observed relative layout (room 1 around the origin; brown door on the south side; room 2 beyond the brown door)...

✅ Evaluation Results

id: 8d54e45588e4d433

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: green chair, laptop, shoppingcart. 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: ['shoppingcart', 'laptop', 'green chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: d79c067009e69829)

❓ 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: green chair, plant, scooter, truck.
...

🤔 Assistant Answer (click to toggle)
THINK:
We never used Query(), so exact coordinates weren’t directly given; I’ll place all *observed* items on an integer grid in a way that is consistent with the observed bearings/distances (binned) from the start and from the brown door. Then, for the rotation-order question, I’ll compute each tar...

✅ Evaluation Results

id: d79c067009e69829

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: green chair, plant, scooter, truck. 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: ['truck', 'green chair', 'plant', 'scooter']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 6888c41ba1414d8a)

❓ 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: green chair, plant, truck.
List them...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll place all *observed* items on an integer grid consistent with the egocentric bearings/distances and the doorway transitions, then compute each target object’s bearing from the start (0,0). During a counterclockwise rotation from facing north, objects are encountered in order of decreasin...

✅ Evaluation Results

id: 6888c41ba1414d8a

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: green chair, plant, truck. 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: ['truck', 'green chair', 'plant']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 124fa9437e2c6a92)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (-5, 2) and face west.
What is the egocentric relation of airplane?

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

🤔 Assistant Answer (click to toggle)
THINK:
Observed objects: scooter, teddybear, plant, brown door, green chair, cap, toilet, television, truck. No Query() actions were used, so absolute coordinates are not given; I’m providing a single consistent integer-coordinate placement that fits the observed egocentric bearings/distances and th...

✅ Evaluation Results

id: 124fa9437e2c6a92

question: Treat the red door as the new 'origin' (0, 0). You move to (-5, 2) and face west. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: 2628716be474f0bc)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (-4, 3) and face east.
What is the egocentric relation of shoppingcart?

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

🤔 Assistant Answer (click to toggle)
THINK:
We never used Query(), so exact coordinates aren’t recoverable; I’m providing one consistent integer layout that matches the observed egocentric bearings/distances (approx.). The red door and shoppingcart were never observed, so shoppingcart’s egocentric relation under the new-origin scenario...

✅ Evaluation Results

id: 2628716be474f0bc

question: Treat the red door as the new 'origin' (0, 0). You move to (-4, 3) and face east. What is the egocentric relation of shoppingcart? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 3 (ID: 74c4c67f29139097)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (1, 2) and face south.
What is the egocentric relation of plant?

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

🤔 Assistant Answer (click to toggle)
THINK:
Need a consistent integer-coordinate global map (origin at initial agent pose). No Query() was used, so coordinates must be inferred approximately from the binned bearings/distances and the few proximity constraints (e.g., truck near teddybear; green chair near cap/toilet). Then answer egocen...

✅ Evaluation Results

id: 74c4c67f29139097

question: Still treat your initial position as origin (0, 0) You move to (1, 2) and face south. What is the egocentric relation of plant? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 4433d7003fa00dbc)

❓ Evaluation Question (click to toggle)
You move to a new location and face west.
You observe: airplane is at front-right, near, facing forward; backpack is at front-left, near, facing backward
Treat the red door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

Answer format: (x, y)
Example: (2, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
We never queried exact coordinates, so the global cognitive map below is a best-effort integer reconstruction consistent with the binned bearings/distances and later cross-checks (notably matching scooter from both the origin view and the brown-door view).
For the question, since the red do...

✅ Evaluation Results

id: 4433d7003fa00dbc

question: You move to a new location and face west. You observe: airplane is at front-right, near, facing forward; backpack is at front-left, near, facing backward Treat the red door as the new 'origin' (0, 0). What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-5, 2]

final_pos: [2, 12]

final_ori: [-1, 0]

room_id: 2

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

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

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.368

raw_score: 0.368

score: 0.368

evaluation_mode: prompt_cogmap

Question 2 (ID: 439fe7bf768dde78)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: shoppingcart is at front, near, facing left; red door is at front-right, slightly far, on front wall
Treat the red door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

Answer format: (x, y)
Example: (2, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
I will output a global (initial-origin) cognitive map containing only objects/doors that were observed. Because no Query() actions were used, absolute integer coordinates are underdetermined from binned bearings/distances; I’m providing a single consistent integer placement that fits the obse...

✅ Evaluation Results

id: 439fe7bf768dde78

question: You move to a new location and face east. You observe: shoppingcart is at front, near, facing left; red door is at front-right, slightly far, on front wall Treat the red door as the new 'origin' (0, 0). What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-4, 3]

final_pos: [3, 13]

final_ori: [1, 0]

room_id: 2

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

final_observation: [{'name': 'shoppingcart', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'red door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.639

raw_score: 0.639

score: 0.639

evaluation_mode: prompt_cogmap

Question 3 (ID: 55cf256e6a550f6e)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: plant is at front, mid distance; brown door is at front-slight-right, slightly far, on front 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)
...

🤔 Assistant Answer (click to toggle)
THINK:
I need to output a global cognitive map (only observed entities) in the initial global frame (start at (0,0), facing north). Since no Query() actions were used, exact integer coordinates aren’t uniquely determined by the binned observations; I’ll provide a single internally-consistent integer...

✅ Evaluation Results

id: 55cf256e6a550f6e

question: You move to a new location and face south. You observe: plant is at front, mid distance; brown door is at front-slight-right, slightly far, on front 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: [1, 2]

final_pos: [13, 12]

final_ori: [0, -1]

room_id: 1

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

final_observation: [{'name': 'plant', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'brown door', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.199

raw_score: 0.199

score: 0.199

evaluation_mode: prompt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: 583ff194ce53a312)

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

🤔 Assistant Answer (click to toggle)
THINK:
Shoppingcart is 8 units directly left (west) of scooter; 8 falls in the slightly far bin.
FINAL ANSWER:
west, slightly far...

✅ Evaluation Results

id: 583ff194ce53a312

question: You return to your starting position and face north. From a Top-Down map, describe where shoppingcart is relative to scooter. 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: 47fca34d599857c1)

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

🤔 Assistant Answer (click to toggle)
THINK:
Truck is 2 units west and 8 units north of the green chair, which bins to north; distance is about 8.25, which bins to far.
FINAL ANSWER:
north, far...

✅ Evaluation Results

id: 47fca34d599857c1

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

action: None

answer: north, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 108ff9b4dbbe7799)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from truck (-3,1) to shoppingcart (-7,3): (-4,+2) which points north-west; distance sqrt(20)≈4.47 falls in (4,8] slightly far.
FINAL ANSWER:
north west, slightly far...

✅ Evaluation Results

id: 108ff9b4dbbe7799

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

action: None

answer: north west, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: 77b726d65cc6b33c)

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

🤔 Assistant Answer (click to toggle)
THINK:
At toilet (-3,-7) facing south, cap at (-1,-9) is 2m east and 2m south: in ego frame that is forward and left; distance √8 ≈ 2.83 → mid distance.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: 77b726d65cc6b33c

question: Now you jump to toilet's direction, facing its direction. Describe where cap 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 2 (ID: 25bf253d96fdcb35)

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

🤔 Assistant Answer (click to toggle)
THINK:
At backpack (-11,1) facing east, laptop at (-7,-1) is 4 forward and 2 to the right; distance √20 ≈ 4.47.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: 25bf253d96fdcb35

question: Now you jump to backpack's direction, facing its direction. Describe where laptop 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 3 (ID: 49542b54da7d1812)

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent is at television (-5,-9) facing north; toilet at (-3,-7) is 2 east and 2 north, which is front-right at mid distance.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 49542b54da7d1812

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

action: None

answer: front-right, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: f90bc1e2211507f1)

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

🤔 Assistant Answer (click to toggle)
THINK:
Scooter is observed “facing forward”, so my current facing direction matches scooter’s true facing (east). The object I’m standing at must therefore face east. Testing east-facing candidates, standing at the truck (-3,1) while facing east places the scooter (1,3) ahead-left at ~4.47m (slightl...

✅ Evaluation Results

id: f90bc1e2211507f1

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

action: None

answer:

answer: truck

final_pos: [9, 11]

final_ori: [1, 0]

final_observation: [{'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'plant', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 52fa8addbc3250f7)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that shoppingcart is front-left, slightly far, facing left; laptop is front-right, slightly far, facing left; red door is front-slight-right, slightly far, on front wall.
Which object are you standing at?
Answer format: <object_...

🤔 Assistant Answer (click to toggle)
THINK:
Check which object’s position and facing (east) makes shoppingcart appear front-left, laptop front-right, and red door front-slight-right at slightly-far distances. From backpack (-11,1) facing east: vectors to shoppingcart (4,2), laptop (4,-2), red door (6,-1) match the observed bearings and...

✅ Evaluation Results

id: 52fa8addbc3250f7

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

action: None

answer:

answer: backpack

final_pos: [1, 11]

final_ori: [1, 0]

final_observation: [{'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'laptop', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'red door', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 2

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 0164361f1d8a8c03)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that toilet is front-right, mid distance, facing backward; brown door is front-right, 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 television position (-5, -9) facing north, the toilet at (-3, -7) lies to the front-right at mid distance and faces south (backward). The brown door at (-1, -3) is also front-right at slightly far distance.
FINAL ANSWER:
television...

✅ Evaluation Results

id: 0164361f1d8a8c03

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

action: None

answer:

answer: television

final_pos: [7, 1]

final_ori: [0, 1]

final_observation: [{'name': 'toilet', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'brown door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 3

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 63c750790aa08aed)

❓ 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, slightly far.
3. Jump to the object at front-left, mid distance.

After executing the actions, what is the ego relation of backpack relative to you?

An...

🤔 Assistant Answer (click to toggle)
THINK:
After Rotate(-90) the agent faces west, jumps to the red door at (-5,0), then jumps to the laptop at (-7,-1). From (-7,-1) facing west, the backpack at (-11,1) is ahead and to the right at distance ~4.47.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: 63c750790aa08aed

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, slightly far. 3. Jump to the object at front-left, mid distance. After executing the actions, what is the ego relation of backpack relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 6412baabea12ca22)

❓ 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, slightly far.
3. Jump to the object at front-slight-right, slightly far.
4. Rotate(-180)

After executing the actions, what is the ego relation of lapto...

🤔 Assistant Answer (click to toggle)
THINK:
Compute final pose after actions, then convert laptop’s displacement into egocentric bearing (relative to final facing east) and binned distance.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: 6412baabea12ca22

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, slightly far. 3. Jump to the object at front-slight-right, slightly far. 4. Rotate(-180) After executing the actions, what is the ego relation of laptop relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: ea1bd9387f8ac037)

❓ 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-slight-right, mid distance.
3. Jump to the object at front, mid distance.
4. Rotate(-180)

After executing the actions, what is the ego relation of brow...

🤔 Assistant Answer (click to toggle)
THINK:
After the moves, you end at (-1, -7) facing north; the brown door is at (-1, -3), directly ahead 4 units away, which bins to mid distance.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: ea1bd9387f8ac037

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-slight-right, mid distance. 3. Jump to the object at front, mid distance. 4. Rotate(-180) After executing the actions, what is the ego relation of brown door relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 6c52792d9caa463c)

❓ 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:
scooter is at front-left, slightly far, facing forward; plant is at front-slight-right, slightly far; brown door is at front-r...

🤔 Assistant Answer (click to toggle)
THINK:
Final view matches being at the red door position (-5, 0) while facing east. From the start (0,0) facing north: rotate to face west so the red door is in FOV, jump to it, then rotate 180° to face east.
FINAL ANSWER:
Rotate(-90), JumpTo(red door), Rotate(180)...

✅ Evaluation Results

id: 6c52792d9caa463c

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: scooter is at front-left, slightly far, facing forward; plant is at front-slight-right, slightly far; brown door is at front-right, slightly far, 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: [7, 10]

final_ori: [1, 0]

room_id: [2, 1]

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', -90], ['jumpto', 'initial_pos'], ['rotate', 180]]

final_observation: [{'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'plant', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'brown door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on right wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [7.0, 10.0]

final_ori: [1, 0]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 3732a26a9f623bf3)

❓ 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:
plant is at front-slight-right, slightly far; scooter is at front-left, slightly far, facing forward; truck is at front-left, ...

🤔 Assistant Answer (click to toggle)
THINK:
Final view implies agent is facing east and located at the red door position (-5,0); from there plant is slightly ahead-right, scooter and truck are ahead-left at the given distance bins. From start (0,0) facing north, rotate to face east then jump to the red door.
FINAL ANSWER:
Rotate(90), J...

✅ Evaluation Results

id: 3732a26a9f623bf3

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: plant is at front-slight-right, slightly far; scooter is at front-left, slightly far, facing forward; truck is at front-left, 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: [7, 10]

final_ori: [1, 0]

room_id: [2, 1]

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', -90], ['jumpto', 'initial_pos'], ['rotate', 180]]

final_observation: [{'name': 'plant', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

error: target_not_visible

score: 0

evaluation_mode: use_gt_cogmap

Question 3 (ID: ea10b24ed30f3b20)

❓ 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:
toilet is at front-right, mid distance, facing left; television is at front, mid distance, facing right

What action sequence ...

🤔 Assistant Answer (click to toggle)
THINK:
Final pose must be at cap’s coordinates (-1,-9) facing west so that television (-5,-9) is directly ahead (front) and toilet (-3,-7) is at 45° to the front-right; rotate first (since JumpTo can’t be first), then jump to cap.
FINAL ANSWER:
Rotate(-90), JumpTo(cap)...

✅ Evaluation Results

id: ea10b24ed30f3b20

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: toilet is at front-right, mid distance, facing left; television is at front, mid distance, facing right 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: [11, 1]

final_ori: [-1, 0]

room_id: 3

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'brown door'], ['jumpto', 'cap'], ['rotate', 90]]

final_observation: [{'name': 'toilet', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing left'}, {'name': 'television', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

error: target_not_visible

score: 0

evaluation_mode: use_gt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 8af89065720ad8db)

❓ 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: shoppingcart, scooter, laptop.
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 for the requested objects in order.
FINAL ANSWER:
(-7, 3); (1, 3); (-7, -1)...

✅ Evaluation Results

id: 8af89065720ad8db

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

action: None

answer: [[-7, 3], [1, 3], [-7, -1]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: f662c4ad95bb2232)

❓ 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: truck, green chair, teddybear, scooter, plant.
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:
(-3, 1); (-1, -7); (-3, 3); (1, 3); (1, -2)...

✅ Evaluation Results

id: f662c4ad95bb2232

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

action: None

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

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: a8bd9bc1b10b893e)

❓ 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: truck, shoppingcart, plant.
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 (allocentric, origin at agent start) for the requested objects in the specified order.
FINAL ANSWER:
(-3, 1); (-7, 3); (1, -2)...

✅ Evaluation Results

id: a8bd9bc1b10b893e

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

action: None

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

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 8d54e45588e4d433)

❓ 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: green chair, laptop, shoppingcart.
L...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s bearing from the origin relative to facing north (bearing = atan2(dx, dy), + clockwise). During a counterclockwise rotation, headings decrease from 0° toward -360°, so objects are encountered in order of decreasing (more negative) bearing starting from closest to 0°. Bea...

✅ Evaluation Results

id: 8d54e45588e4d433

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: green chair, laptop, shoppingcart. 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: ['shoppingcart', 'laptop', 'green chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: d79c067009e69829)

❓ 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: green chair, plant, scooter, truck.
...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s bearing from the origin (0,0) relative to facing north, then list them in the sequence encountered as the heading rotates counterclockwise from 0° through -360°: truck (-71.6°), green chair (-171.9°), plant (153.4° ≡ -206.6°), scooter (18.4° ≡ -341.6°).
FINAL ANSWER:
tru...

✅ Evaluation Results

id: d79c067009e69829

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: green chair, plant, scooter, truck. 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: ['truck', 'green chair', 'plant', 'scooter']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 6888c41ba1414d8a)

❓ 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: green chair, plant, truck.
List them...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s bearing from the origin with 0° at north and positive clockwise, then order by when a counterclockwise rotation (heading decreasing, wrapping at -180/180) aligns to each bearing: truck (-71.6°) first, green chair (-171.9°) next, plant (+153.4°) after wrap.
FINAL ANSWER:
...

✅ Evaluation Results

id: 6888c41ba1414d8a

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: green chair, plant, truck. 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: ['truck', 'green chair', 'plant']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 124fa9437e2c6a92)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (-5, 2) and face west.
What is the egocentric relation of airplane?

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute airplane position relative to agent, then convert to agent-facing (west) egocentric direction and distance bin.
FINAL ANSWER:
front-right, near...

✅ Evaluation Results

id: 124fa9437e2c6a92

question: Treat the red door as the new 'origin' (0, 0). You move to (-5, 2) and face west. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 2628716be474f0bc)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (-4, 3) and face east.
What is the egocentric relation of shoppingcart?

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

🤔 Assistant Answer (click to toggle)
THINK:
With red door as origin, agent at (1,3) facing east and shoppingcart at (-2,3), so shoppingcart is 3 units directly behind the agent, outside the ±45° FOV; distance bin is mid distance.
FINAL ANSWER:
beyond-fov, mid distance...

✅ Evaluation Results

id: 2628716be474f0bc

question: Treat the red door as the new 'origin' (0, 0). You move to (-4, 3) and face east. What is the egocentric relation of shoppingcart? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_gt_cogmap

Question 3 (ID: 74c4c67f29139097)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (1, 2) and face south.
What is the egocentric relation of plant?

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (1,2) facing south; plant at (1,-2) is straight ahead by 4 units, which bins to mid distance.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: 74c4c67f29139097

question: Still treat your initial position as origin (0, 0) You move to (1, 2) and face south. What is the egocentric relation of plant? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 4433d7003fa00dbc)

❓ Evaluation Question (click to toggle)
You move to a new location and face west.
You observe: airplane is at front-right, near, facing forward; backpack is at front-left, near, facing backward
Treat the red door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

Answer format: (x, y)
Example: (2, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Using the reference map, airplane is at (-11, 3) and backpack at (-11, 1). Facing west, “front-right” implies the object is west and north of me; “front-left” implies west and south of me. The point one step east of x=-11 and midway in y is (-10, 2). Relative to red door at (-5, 0), my coordi...

✅ Evaluation Results

id: 4433d7003fa00dbc

question: You move to a new location and face west. You observe: airplane is at front-right, near, facing forward; backpack is at front-left, near, facing backward Treat the red door as the new 'origin' (0, 0). What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-5, 2]

final_pos: [2, 12]

final_ori: [-1, 0]

room_id: 2

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

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

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 439fe7bf768dde78)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: shoppingcart is at front, near, facing left; red door is at front-right, slightly far, on front wall
Treat the red door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

Answer format: (x, y)
Example: (2, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Use the inferred absolute positions: agent at (-9, 3) and red door at (-5, 0). Re-express agent relative to red door by subtracting red door coordinates: (-9+5, 3-0)=(-4, 3).
FINAL ANSWER:
(-4, 3)...

✅ Evaluation Results

id: 439fe7bf768dde78

question: You move to a new location and face east. You observe: shoppingcart is at front, near, facing left; red door is at front-right, slightly far, on front wall Treat the red door as the new 'origin' (0, 0). What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-4, 3]

final_pos: [3, 13]

final_ori: [1, 0]

room_id: 2

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

final_observation: [{'name': 'shoppingcart', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'red door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 55cf256e6a550f6e)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: plant is at front, mid distance; brown door is at front-slight-right, slightly far, on front 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)
...

🤔 Assistant Answer (click to toggle)
THINK:
Facing south, “plant at front, mid distance” fits agent directly north of plant (1,-2) by 4 units → agent at (1,2). This also makes brown door (-1,-3) front-slight-right and slightly far.
FINAL ANSWER:
(1, 2)...

✅ Evaluation Results

id: 55cf256e6a550f6e

question: You move to a new location and face south. You observe: plant is at front, mid distance; brown door is at front-slight-right, slightly far, on front 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: [1, 2]

final_pos: [13, 12]

final_ori: [0, -1]

room_id: 1

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

final_observation: [{'name': 'plant', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'brown door', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: 583ff194ce53a312)

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

🤔 Assistant Answer (click to toggle)
THINK:
Shoppingcart was never observed, so its position relative to scooter cannot be determined from the built map.
FINAL ANSWER:
unknown, unknown...

✅ Evaluation Results

id: 583ff194ce53a312

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

action: None

answer: west, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: 47fca34d599857c1)

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

🤔 Assistant Answer (click to toggle)
THINK: Compute vector from green chair to truck: (-5, +11) => north-west direction; distance sqrt(146)≈12.1 falls in (8,16] => far.
FINAL ANSWER:
north-west, far...

✅ Evaluation Results

id: 47fca34d599857c1

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

action: None

answer: north, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 3 (ID: 108ff9b4dbbe7799)

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

🤔 Assistant Answer (click to toggle)
THINK:
Shoppingcart was never observed during exploration, so its position relative to the truck cannot be determined from the built map.
FINAL ANSWER:
unknown, unknown...

✅ Evaluation Results

id: 108ff9b4dbbe7799

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

action: None

answer: north west, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: 77b726d65cc6b33c)

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

🤔 Assistant Answer (click to toggle)
THINK:
After jumping to the toilet and facing south (toilet’s facing), the cap is 2 units forward and 2 units left; distance √8 ≈ 2.83 → mid distance.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: 77b726d65cc6b33c

question: Now you jump to toilet's direction, facing its direction. Describe where cap 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 2 (ID: 25bf253d96fdcb35)

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

🤔 Assistant Answer (click to toggle)
THINK: Laptop was never observed/mapped, and backpack was never observed; after a hypothetical jump, laptop’s relative direction/distance cannot be determined from available information.
FINAL ANSWER:
beyond-fov, extremely far...

✅ Evaluation Results

id: 25bf253d96fdcb35

question: Now you jump to backpack's direction, facing its direction. Describe where laptop 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

evaluation_mode: use_model_cogmap

Question 3 (ID: 49542b54da7d1812)

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

🤔 Assistant Answer (click to toggle)
THINK:
After jumping to the television and facing the television’s facing direction (north), the toilet at (-2,-6) is offset by (+1,+2), which is front-right at ~2.24 units (mid distance).
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 49542b54da7d1812

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

action: None

answer: front-right, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: f90bc1e2211507f1)

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

🤔 Assistant Answer (click to toggle)
THINK: Scooter is facing east (“forward”), so I must be at an east-facing object; the configuration scooter front-left and plant front-right best matches being at the teddybear.
FINAL ANSWER:
teddybear...

✅ Evaluation Results

id: f90bc1e2211507f1

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

action: None

answer:

answer: truck

final_pos: [9, 11]

final_ori: [1, 0]

final_observation: [{'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'plant', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: 52fa8addbc3250f7)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that shoppingcart is front-left, slightly far, facing left; laptop is front-right, slightly far, facing left; red door is front-slight-right, slightly far, on front wall.
Which object are you standing at?
Answer format: <object_...

🤔 Assistant Answer (click to toggle)
THINK:
After jumping, the object you’re standing on is not listed in the observation. In the third room, the newly revealed items are shoppingcart, laptop, and the red door, leaving airplane/backpack as the only plausible stand-on objects; the symmetric front-left/front-right placement suggests a ce...

✅ Evaluation Results

id: 52fa8addbc3250f7

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

action: None

answer:

answer: backpack

final_pos: [1, 11]

final_ori: [1, 0]

final_observation: [{'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'laptop', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'red door', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 2

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: 0164361f1d8a8c03)

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

🤔 Assistant Answer (click to toggle)
THINK: If I’m standing at the television and (after the jump) facing in the television’s facing direction (south), then the toilet (which faces north) would appear front-right and “facing backward,” and the brown door (to the southwest, on the room’s front/south wall) would also appear front-right.
...

✅ Evaluation Results

id: 0164361f1d8a8c03

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

action: None

answer:

answer: television

final_pos: [7, 1]

final_ori: [0, 1]

final_observation: [{'name': 'toilet', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'brown door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

scooter: [13.0, 13.0]

shoppingcart: [5.0, 13.0]

green chair: [11.0, 3.0]

truck: [9.0, 11.0]

airplane: [1.0, 13.0]

cap: [11.0, 1.0]

teddybear: [9.0, 13.0]

plant: [13.0, 8.0]

laptop: [5.0, 9.0]

toilet: [9.0, 3.0]

backpack: [1.0, 11.0]

television: [7.0, 1.0]

red door: [7.0, 10.0]

brown door: [11.0, 7.0]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

room_id: 3

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 63c750790aa08aed)

❓ 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, slightly far.
3. Jump to the object at front-left, mid distance.

After executing the actions, what is the ego relation of backpack relative to you?

An...

🤔 Assistant Answer (click to toggle)
THINK: The final jump targets the backpack (front-left, mid distance), so you end up at the backpack’s exact location; its distance to you is zero.
FINAL ANSWER:
front, same distance...

✅ Evaluation Results

id: 63c750790aa08aed

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, slightly far. 3. Jump to the object at front-left, mid distance. After executing the actions, what is the ego relation of backpack relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: 6412baabea12ca22)

❓ 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, slightly far.
3. Jump to the object at front-slight-right, slightly far.
4. Rotate(-180)

After executing the actions, what is the ego relation of lapto...

🤔 Assistant Answer (click to toggle)
THINK: The laptop was never encountered along the described jumps from the start area, so it remains outside the final 90° field of view; its exact range cannot be fixed from the given information, but it is not nearby.
FINAL ANSWER:
beyond-fov, far...

✅ Evaluation Results

id: 6412baabea12ca22

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, slightly far. 3. Jump to the object at front-slight-right, slightly far. 4. Rotate(-180) After executing the actions, what is the ego relation of laptop relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: ea1bd9387f8ac037)

❓ 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-slight-right, mid distance.
3. Jump to the object at front, mid distance.
4. Rotate(-180)

After executing the actions, what is the ego relation of brow...

🤔 Assistant Answer (click to toggle)
THINK:
From the start facing north, rotating -180 makes me face south; the front-slight-right mid object is the brown door, so I jump there (still facing south). From the brown door, the front mid object is the green chair, so I jump there (still facing south). Rotating -180 makes me face north agai...

✅ Evaluation Results

id: ea1bd9387f8ac037

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-slight-right, mid distance. 3. Jump to the object at front, mid distance. 4. Rotate(-180) After executing the actions, what is the ego relation of brown door relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 6c52792d9caa463c)

❓ 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:
scooter is at front-left, slightly far, facing forward; plant is at front-slight-right, slightly far; brown door is at front-r...

🤔 Assistant Answer (click to toggle)
THINK:
End at the teddybear (visible from the start), then turn to face east so the brown door is on the right wall and scooter/plant fall into the front-left/front-right sectors.
FINAL ANSWER:
Rotate(0), JumpTo(teddybear), Rotate(90)...

✅ Evaluation Results

id: 6c52792d9caa463c

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: scooter is at front-left, slightly far, facing forward; plant is at front-slight-right, slightly far; brown door is at front-right, slightly far, 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: [7, 10]

final_ori: [1, 0]

room_id: [2, 1]

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', -90], ['jumpto', 'initial_pos'], ['rotate', 180]]

final_observation: [{'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'plant', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'brown door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on right wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [9.0, 13.0]

final_ori: [1, 0]

visible_match: False

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: 3732a26a9f623bf3)

❓ 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:
plant is at front-slight-right, slightly far; scooter is at front-left, slightly far, facing forward; truck is at front-left, ...

🤔 Assistant Answer (click to toggle)
THINK: From the start (facing north), rotate to face the wall with the brown door, jump to it, then rotate to face back into the room where scooter/truck/plant are visible.
FINAL ANSWER:
Rotate(180), JumpTo(brown door), Rotate(180)...

✅ Evaluation Results

id: 3732a26a9f623bf3

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: plant is at front-slight-right, slightly far; scooter is at front-left, slightly far, facing forward; truck is at front-left, 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: [7, 10]

final_ori: [1, 0]

room_id: [2, 1]

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', -90], ['jumpto', 'initial_pos'], ['rotate', 180]]

final_observation: [{'name': 'plant', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'scooter', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'truck', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: False

final_pos: [11.0, 7.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: ea10b24ed30f3b20)

❓ 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:
toilet is at front-right, mid distance, facing left; television is at front, mid distance, facing right

What action sequence ...

🤔 Assistant Answer (click to toggle)
THINK: From the start (room 1), first rotate to bring the brown door into view, jump to the doorway to access room 2 objects, then jump to the cap (in room 2) and rotate to the final facing so television is front and toilet is front-right.
FINAL ANSWER:
Rotate(180), JumpTo(brown door), JumpTo(cap), ...

✅ Evaluation Results

id: ea10b24ed30f3b20

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: toilet is at front-right, mid distance, facing left; television is at front, mid distance, facing right 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: [11, 1]

final_ori: [-1, 0]

room_id: 3

init_pos: [12, 10]

init_ori: [0, 1]

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'brown door'], ['jumpto', 'cap'], ['rotate', 90]]

final_observation: [{'name': 'toilet', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing left'}, {'name': 'television', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: False

final_pos: [11.0, 1.0]

final_ori: [1, 0]

visible_match: False

score: 0

evaluation_mode: use_model_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 8af89065720ad8db)

❓ 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: shoppingcart, scooter, laptop.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK: Only scooter was observed; shoppingcart and laptop were not observed, so their coordinates cannot be determined from the exploration data.
FINAL ANSWER:
(null, null); (1, 3); (null, null)...

✅ Evaluation Results

id: 8af89065720ad8db

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

action: None

answer: [[-7, 3], [1, 3], [-7, -1]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

error: mismatched_coordinate_count

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: f662c4ad95bb2232)

❓ 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: truck, green chair, teddybear, scooter, plant.
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 start, x east/right, y north/up) and list the requested objects in the given order.
FINAL ANSWER:
(-5, 5); (0, -6); (-3, 5); (1, 3); (2, -2)...

✅ Evaluation Results

id: f662c4ad95bb2232

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

action: None

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

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.592

threshold: 0.900

score: 0.592

evaluation_mode: use_model_cogmap

Question 3 (ID: a8bd9bc1b10b893e)

❓ 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: truck, shoppingcart, plant.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK: Truck and plant were observed and placed in the global map relative to the origin; shoppingcart was never observed so its coordinates cannot be determined.
FINAL ANSWER:
(-5, 5); (unknown, unknown); (2, -2)...

✅ Evaluation Results

id: a8bd9bc1b10b893e

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

action: None

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

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

error: mismatched_coordinate_count

score: 0

evaluation_mode: use_model_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 8d54e45588e4d433)

❓ 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: green chair, laptop, shoppingcart.
L...

🤔 Assistant Answer (click to toggle)
THINK: As I rotate counterclockwise from north, objects with more negative bearings appear first (westward), then south, then eastward; thus the sequence is laptop (west), then green chair (south), then shoppingcart (east).
FINAL ANSWER:
laptop, green chair, shoppingcart...

✅ Evaluation Results

id: 8d54e45588e4d433

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: green chair, laptop, shoppingcart. 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: ['shoppingcart', 'laptop', 'green chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: d79c067009e69829)

❓ 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: green chair, plant, scooter, truck.
...

🤔 Assistant Answer (click to toggle)
THINK: Compute each object’s bearing from the origin, convert to counterclockwise-from-north sweep order (north→west→south→east→north), then sort ascending by that CCW angle.
FINAL ANSWER:
truck, green chair, plant, scooter...

✅ Evaluation Results

id: d79c067009e69829

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: green chair, plant, scooter, truck. 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: ['truck', 'green chair', 'plant', 'scooter']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 6888c41ba1414d8a)

❓ 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: green chair, plant, truck.
List them...

🤔 Assistant Answer (click to toggle)
THINK:
Compute bearings from origin (facing north): truck at 315° (northwest), green chair at 180° (south), plant at 135° (southeast). During counterclockwise rotation starting from north, directly-ahead crossings occur in descending bearing order: 315°, then 180°, then 135°.
FINAL ANSWER:
truck, gr...

✅ Evaluation Results

id: 6888c41ba1414d8a

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: green chair, plant, truck. 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: ['truck', 'green chair', 'plant']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 124fa9437e2c6a92)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (-5, 2) and face west.
What is the egocentric relation of airplane?

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

🤔 Assistant Answer (click to toggle)
THINK:
The airplane and the red door were never observed, so airplane’s position relative to the red door (new origin) is undefined from the available map; thus its egocentric direction/distance from the stated agent pose cannot be determined.
FINAL ANSWER:
unknown, unknown...

✅ Evaluation Results

id: 124fa9437e2c6a92

question: Treat the red door as the new 'origin' (0, 0). You move to (-5, 2) and face west. What is the egocentric relation of airplane? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: 2628716be474f0bc)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (-4, 3) and face east.
What is the egocentric relation of shoppingcart?

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

🤔 Assistant Answer (click to toggle)
THINK: shoppingcart was never observed, and the red door’s position is unknown, so its egocentric relation from the given pose cannot be determined.
FINAL ANSWER:
unknown, unknown...

✅ Evaluation Results

id: 2628716be474f0bc

question: Treat the red door as the new 'origin' (0, 0). You move to (-4, 3) and face east. What is the egocentric relation of shoppingcart? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: 74c4c67f29139097)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (1, 2) and face south.
What is the egocentric relation of plant?

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

🤔 Assistant Answer (click to toggle)
THINK: From (1,2) facing south, plant at (2,-2) is mostly ahead (south) and slightly to the left; distance is just over 4.
FINAL ANSWER:
front-slight-left, slightly far...

✅ Evaluation Results

id: 74c4c67f29139097

question: Still treat your initial position as origin (0, 0) You move to (1, 2) and face south. What is the egocentric relation of plant? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 4433d7003fa00dbc)

❓ Evaluation Question (click to toggle)
You move to a new location and face west.
You observe: airplane is at front-right, near, facing forward; backpack is at front-left, near, facing backward
Treat the red door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

Answer format: (x, y)
Example: (2, -1)
...

🤔 Assistant Answer (click to toggle)
THINK: With the red door defined as the new origin and no additional offset provided, the only determinate interpretation is that my current position is at the red door.
FINAL ANSWER:
(0, 0)...

✅ Evaluation Results

id: 4433d7003fa00dbc

question: You move to a new location and face west. You observe: airplane is at front-right, near, facing forward; backpack is at front-left, near, facing backward Treat the red door as the new 'origin' (0, 0). What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-5, 2]

final_pos: [2, 12]

final_ori: [-1, 0]

room_id: 2

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

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

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.368

raw_score: 0.368

score: 0.368

evaluation_mode: use_model_cogmap

Question 2 (ID: 439fe7bf768dde78)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: shoppingcart is at front, near, facing left; red door is at front-right, slightly far, on front wall
Treat the red door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

Answer format: (x, y)
Example: (2, -1)
...

🤔 Assistant Answer (click to toggle)
THINK: Red door is southeast (front-right) of me while I face east, at a slightly-far distance; approximate offset from me to door as (4, -4), so my position relative to door is (-4, 4).
FINAL ANSWER:
(-4, 4)...

✅ Evaluation Results

id: 439fe7bf768dde78

question: You move to a new location and face east. You observe: shoppingcart is at front, near, facing left; red door is at front-right, slightly far, on front wall Treat the red door as the new 'origin' (0, 0). What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [-4, 3]

final_pos: [3, 13]

final_ori: [1, 0]

room_id: 2

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

final_observation: [{'name': 'shoppingcart', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'red door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.819

raw_score: 0.819

score: 0.819

evaluation_mode: use_model_cogmap

Question 3 (ID: 55cf256e6a550f6e)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: plant is at front, mid distance; brown door is at front-slight-right, slightly far, on front 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)
...

🤔 Assistant Answer (click to toggle)
THINK: Use earlier inferred global coordinates consistent with observations: plant at (1, -2) and brown door at (-1, -3). New pose faces south with plant straight ahead at mid distance ⇒ same x as plant and y = -2 + 4 = 2. Check door from (1,2) gives front-slight-right and slightly-far, matching.
FI...

✅ Evaluation Results

id: 55cf256e6a550f6e

question: You move to a new location and face south. You observe: plant is at front, mid distance; brown door is at front-slight-right, slightly far, on front 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: [1, 2]

final_pos: [13, 12]

final_ori: [0, -1]

room_id: 1

object_positions:

scooter: [13, 13]

shoppingcart: [5, 13]

green chair: [11, 3]

truck: [9, 11]

airplane: [1, 13]

cap: [11, 1]

teddybear: [9, 13]

plant: [13, 8]

laptop: [5, 9]

toilet: [9, 3]

backpack: [1, 11]

television: [7, 1]

red door: [7, 10]

brown door: [11, 7]

object_orientations:

scooter: [1, 0]

shoppingcart: [0, 1]

green chair: [1, 0]

truck: [1, 0]

airplane: [-1, 0]

cap: [0, -1]

teddybear: [1, 0]

laptop: [0, 1]

toilet: [0, -1]

backpack: [1, 0]

television: [0, 1]

red door: [1, 0]

brown door: [0, 1]

final_observation: [{'name': 'plant', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'brown door', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

gate_info:

red door:

room_ids: [2, 1]

ori_by_room:

2: [-1, 0]

1: [1, 0]

brown door:

room_ids: [3, 1]

ori_by_room:

3: [0, -1]

1: [0, 1]

object_rooms:

scooter: 1

shoppingcart: 2

green chair: 3

truck: 1

airplane: 2

cap: 3

teddybear: 1

plant: 1

laptop: 2

toilet: 3

backpack: 2

television: 3

red door: [2, 1]

brown door: [3, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_model_cogmap

Model: gpt-5.2

sample_run04

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

🧭 False Belief Exploration

🔄 FB Turn 21

🔄 FB Turn 22

🔄 FB Turn 23

🔄 FB Turn 24

🔄 FB Turn 25

🔄 FB Turn 26

🔄 FB Turn 27

🔄 FB Turn 28

🔄 FB Turn 29

🔄 FB Turn 30

🔄 FB Turn 31

📊 Evaluation (default)

📊 Task: DirectionEvaluationTask

Question 1 (ID: 583ff194ce53a312)

Question 2 (ID: 47fca34d599857c1)

Question 3 (ID: 108ff9b4dbbe7799)

📊 Task: PovEvaluationTask

Question 1 (ID: 77b726d65cc6b33c)

Question 2 (ID: 25bf253d96fdcb35)

Question 3 (ID: 49542b54da7d1812)

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: f90bc1e2211507f1)

Question 2 (ID: 52fa8addbc3250f7)

Question 3 (ID: 0164361f1d8a8c03)

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 63c750790aa08aed)

Question 2 (ID: 6412baabea12ca22)

Question 3 (ID: ea1bd9387f8ac037)

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 6c52792d9caa463c)

Question 2 (ID: 3732a26a9f623bf3)

Question 3 (ID: ea10b24ed30f3b20)

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 8af89065720ad8db)

Question 2 (ID: f662c4ad95bb2232)