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) facing north to gather the first set of visible objects/doors and their relationships with minimal cost....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Infer a plausible integer position for the laptop consistent with "front-left, mid distance" from agent at origin facing north; map "facing backward" to global south.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "north" },
"laptop": { "position": [-2, 2], "facing": "south" }
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -2,
      2
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Place the laptop at an integer coordinate consistent with front-left and mid-distance; encode its facing as backward (-y) in the agent-relative frame.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"laptop": {
"position": [-2, 3],
"facing": "-y"
}
}
}...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: Only the laptop has been observed; the point likely corresponding to it (B) is explored, while the other candidate points lie in still-unobserved areas.
FINAL ANSWER: {"unexplored":["A","C","D","E","F"]}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, C, D, E, F

🎯 Ground Truth: F, D, B

🧠 Cognitive Map Metrics

Global:

dir: 1

facing: 1

pos: 0.882

overall: 0.961

Global (Full):

dir: 0.025

facing: 0.200

pos: 0.118

overall: 0.114

Local:

dir: 1

facing: 1

pos: 0.779

overall: 0.926

Local (Newly):

dir: 1

facing: 1

pos: 0.779

overall: 0.926

Fog Probe:

precision: 0.400

recall: 0.667

overall: 0.500

📈 Turn Metrics

node_coverage: 0.071

edge_coverage: 0.024

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: 0.057

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 2

👤 Environment Observation (click to toggle)
You observe:
• laptop: front-left, mid distance, facing backward.
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)
Only the laptop is currently visible; to efficiently expand coverage from the same location, rotate to face east and observe a new 90° sector....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a consistent global map from the single observation; approximate the laptop’s binned location as an integer coordinate in the front-left mid-distance region relative to the origin, and set headings to cardinal directions.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "east...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -2,
      2
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: No objects or doors are currently in my field of view after rotating to face east, 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: North-facing observe likely covered points at/near bearings within ±45° (C, E, F). East-facing observe added nothing. Points A (southwest), B (northwest), and D (west) fall outside observed sectors, so they are in unexplored regions.
FINAL ANSWER:
{
"unexplored": ["A", "B", "D"]
}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, D

🎯 Ground Truth: B, D, A

🧠 Cognitive Map Metrics

Global:

dir: 1

facing: 1

pos: 0.882

overall: 0.961

Global (Full):

dir: 0.025

facing: 0.200

pos: 0.118

overall: 0.114

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 1

recall: 1

overall: 1

📈 Turn Metrics

node_coverage: 0.071

edge_coverage: 0.024

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: 0.057

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)
Since east-facing reveals nothing, sweep the next sector by rotating to face south and observe again to locate additional objects/doors....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a minimal consistent global map using integer coordinates that satisfy the observed egocentric bearing/distance bins from the origin, and set agent facing to current (south). Laptop "facing backward" from initial north implies global south.
FINAL ANSWER:
{
"agent": { "position": [0, 0...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      -2,
      3
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      2,
      -3
    ]
  },
  "yellow door": {
    "position": [
      -1,
      -5
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-relative) map from the current FOV; convert binned bearings/distances into approximate integer coordinates; include only the two observed doors.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"red door": {
"position": [-2, 3]
},
"yellow door": {
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "red door": {
      "position": [
        -2,
        3
      ]
    },
    "yellow door": {
      "position": [
        2,
        6
      ]
    }
  }
}

🎯 Ground Truth

{
  "yellow door": {
    "position": [
      1,
      4
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "red door": {
      "position": [
        -2,
        3
      ]
    },
    "yellow door": {
      "position": [
        2,
        6
      ]
    }
  }
}

🎯 Ground Truth

{
  "yellow door": {
    "position": [
      1,
      4
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: C is likely within the already observed northern sector (laptop area), while A, B, D, E, and F lie in regions not yet observed.
FINAL ANSWER:
{"unexplored":["A","B","D","E","F"]}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, D, E, F

🎯 Ground Truth: B, A, C

🧠 Cognitive Map Metrics

Global:

dir: 1

facing: 1

pos: 0.821

overall: 0.940

Global (Full):

dir: 0.083

facing: 0.200

pos: 0.219

overall: 0.167

Local:

dir: 1

facing: 1

pos: 0.718

overall: 0.906

Local (Newly):

dir: 1

facing: 1

pos: 0.718

overall: 0.906

Fog Probe:

precision: 0.400

recall: 0.667

overall: 0.500

📈 Turn Metrics

node_coverage: 0.214

edge_coverage: 0.073

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: 0.160

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 4

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• red door: front-left, mid distance, on left wall
• yellow door: front-slight-right, slightly far, 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 nearer observed doorway (red door) to exploit the doorway visibility rule and potentially see into two rooms at once....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Infer integer coordinates consistent with observed bearings/distances across the three observations; keep agent at the red door facing south; include only observed entities and facing for objects that have it.
FINAL ANSWER:
{
"agent": { "position": [1, -2], "facing": "south" },
"laptop": ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      -2,
      3
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      1,
      -2
    ]
  },
  "yellow door": {
    "position": [
      -1,
      -5
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "south"
  }
}

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

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

{
  "yellow door": {
    "position": [
      2,
      2
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: We have only observed the laptop and two doors; none of the candidate regions A-H have been explored/observed yet, so all labeled candidate points are in unexplored areas.
FINAL ANSWER: {"unexplored":["A","B","C","D","E","F","G","H"]}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F, G, H

🤖 Predicted: A, B, C, D, E, F, G, H

🎯 Ground Truth: E, G, C, A

🧠 Cognitive Map Metrics

Global:

dir: 1

facing: 1

pos: 0.858

overall: 0.953

Global (Full):

dir: 0.083

facing: 0.200

pos: 0.229

overall: 0.171

Local:

dir: 1

facing: 1

pos: 1

overall: 1

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.214

edge_coverage: 0.098

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: 0.191

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 5

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

🤔 Assistant Thinking (click to toggle)
From the red doorway (which should allow visibility into both connected rooms), sweep a different sector by rotating to face west and observe for any objects/doors that were outside the current 90° FOV....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK:
Infer a consistent integer-coordinate placement (relative to initial origin and cardinal north) that satisfies all observed egocentric bearings, distance bins, door-jump constraints, and the desklamp–shoppingcart proximity relation. Set the red door at (1,-2) so the yellow door can be both sl...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "west"
  },
  "laptop": {
    "position": [
      -2,
      3
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      1,
      -2
    ]
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ]
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -4,
      1
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map using integer coordinates consistent with the observed angle/distance bins and the stated proximity constraint (desklamp east, near to shoppingcart).
FINAL ANSWER: {
"origin": "agent",
"objects": {
"shoppingcart": {
"position": [0, 4],
"fa...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shoppingcart": {
      "position": [
        0,
        4
      ],
      "facing": "+x"
    },
    "desklamp": {
      "position": [
        2,
        4
      ],
      "facing": "-y"
    },
    "truck": {
      "position": [
        3,
        5
      ],
      "facing": "+y"
    },
    "yellow door": {
      "position": [
        -2,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "shoppingcart": {
    "position": [
      0,
      4
    ],
    "facing": "north"
  },
  "yellow door": {
    "position": [
      -2,
      2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      2,
      4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      4,
      6
    ],
    "facing": "west"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shoppingcart": {
      "position": [
        0,
        4
      ],
      "facing": "+x"
    },
    "desklamp": {
      "position": [
        2,
        4
      ],
      "facing": "-y"
    },
    "truck": {
      "position": [
        3,
        5
      ],
      "facing": "+y"
    },
    "yellow door": {
      "position": [
        -2,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "shoppingcart": {
    "position": [
      0,
      4
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      2,
      4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      4,
      6
    ],
    "facing": "west"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: A is in the area south of the current viewpoint (not covered by the current/previous north-facing observations), while B lies in the already observed northern area of the right-side room.
FINAL ANSWER: {"unexplored":["A"]}...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#......#
#......#.....B#
#......#......#
#......*......#
#......#A.....#
#####+#########
##......#######
##......#######
##......#######
##......#######
##......#######
##......#######
###############

🔍 Fog Probe JSONs

📍 Candidates: A, B

🤖 Predicted: A

🎯 Ground Truth: B

🧠 Cognitive Map Metrics

Global:

dir: 0.857

facing: 1

pos: 0.875

overall: 0.911

Global (Full):

dir: 0.200

facing: 0.500

pos: 0.408

overall: 0.369

Local:

dir: 0.700

facing: 1

pos: 0.858

overall: 0.853

Local (Newly):

dir: 0.833

facing: 1

pos: 0.866

overall: 0.900

Fog Probe:

precision: 0

recall: 0

overall: 0

📈 Turn Metrics

node_coverage: 0.429

edge_coverage: 0.171

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: 0.382

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 6

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• desklamp: front-right, slightly far, facing backward
• shoppingcart: front, mid distance, facing right
• truck: front-right, slightly far, facing forward
• yellow door: front-left, mid distance, on left wall
Assume your current facing direction is called "n...

🤔 Assistant Thinking (click to toggle)
From the red doorway, I should continue a 360° sweep to discover additional objects in this (and the connected) room(s). Rotating clockwise 90° from the current facing should expose a new 90° sector without moving....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a globally consistent integer-coordinate map (origin at initial agent location), using the observed egocentric bearings/distances across rotations/jumps; assign approximate coordinates that satisfy all qualitative constraints and include facing only where observed.
FINAL ANSWER:
{
"ag...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      2,
      -3
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -2,
      3
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      2,
      -3
    ]
  },
  "yellow door": {
    "position": [
      -1,
      -5
    ]
  },
  "shoppingcart": {
    "position": [
      -2,
      -3
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -3,
      -1
    ],
    "facing": "west"
  },
  "books": {
    "position": [
      6,
      1
    ]
  },
  "pillow": {
    "position": [
      5,
      1
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map for the currently visible objects only, using the agent as origin with +y forward (current facing). Assign consistent integer coordinates that satisfy the observed angle/distance bins and the pillow-west-of-books proximity.
FINAL ANSWER:
{
"origin": "agent"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "laptop": {
      "position": [
        -3,
        4
      ],
      "facing": "-y"
    },
    "pillow": {
      "position": [
        3,
        4
      ]
    },
    "books": {
      "position": [
        5,
        4
      ]
    }
  }
}

🎯 Ground Truth

{
  "pillow": {
    "position": [
      2,
      4
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      -2,
      4
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      4,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "laptop": {
      "position": [
        -3,
        4
      ],
      "facing": "-y"
    },
    "pillow": {
      "position": [
        3,
        4
      ]
    },
    "books": {
      "position": [
        5,
        4
      ]
    }
  }
}

🎯 Ground Truth

{
  "pillow": {
    "position": [
      2,
      4
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      4,
      4
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: The agent’s observations have been confined to the left-side region around the current position/doorway; the candidate points A–F all lie across the interior wall in the right-side region(s) that have not yet been directly observed/covered by FOV, so they are in unexplored/unobserved space.
F...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: A, C, B

🧠 Cognitive Map Metrics

Global:

dir: 0.800

facing: 1

pos: 0.720

overall: 0.840

Global (Full):

dir: 0.300

facing: 0.500

pos: 0.432

overall: 0.411

Local:

dir: 1

facing: 1

pos: 0.838

overall: 0.946

Local (Newly):

dir: 1

facing: 1

pos: 0.838

overall: 0.946

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.571

edge_coverage: 0.244

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: 0.478

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 7

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• laptop: front-left, slightly far, facing backward
• pillow: front-right, slightly far
• books: front-right, slightly far
Assume your current facing direction is called "north":
• pillow is west, near to books.
You have a maximum of 14 exploration steps left...

🤔 Assistant Thinking (click to toggle)
Continue the remaining sweep from this doorway position by rotating another 90° to check the next unseen sector for additional objects (and possibly another door/room boundary)....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assign consistent integer coordinates using the initial pose as origin; use relative bearings/distances across multiple observations (especially from the red doorway) to triangulate approximate positions, and convert observed object-facing (ego) into global cardinal facing.
FINAL ANSWER:
{
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      2,
      -3
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      2,
      -3
    ]
  },
  "yellow door": {
    "position": [
      0,
      -6
    ]
  },
  "laptop": {
    "position": [
      -2,
      3
    ],
    "facing": "south"
  },
  "desklamp": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -2,
      -3
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -3,
      1
    ],
    "facing": "west"
  },
  "books": {
    "position": [
      6,
      3
    ]
  },
  "pillow": {
    "position": [
      4,
      3
    ]
  },
  "backpack": {
    "position": [
      4,
      -3
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      6,
      1
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) map using the current FOV only; assign consistent integer coordinates that satisfy the reported egocentric bearings/distances and the bike–books proximity relation, and include facing where given.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "backpack": {
      "position": [
        0,
        1
      ],
      "facing": "-x"
    },
    "bike": {
      "position": [
        -3,
        6
      ],
      "facing": "-x"
    },
    "books": {
      "position": [
        -5,
        6
      ]
    }
  }
}

🎯 Ground Truth

{
  "bike": {
    "position": [
      -2,
      4
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      0,
      2
    ],
    "facing": "north"
  },
  "books": {
    "position": [
      -4,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "backpack": {
      "position": [
        0,
        1
      ],
      "facing": "-x"
    },
    "bike": {
      "position": [
        -3,
        6
      ],
      "facing": "-x"
    },
    "books": {
      "position": [
        -5,
        6
      ]
    }
  }
}

🎯 Ground Truth

{
  "bike": {
    "position": [
      -2,
      4
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      0,
      2
    ],
    "facing": "north"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.803

facing: 1

pos: 0.754

overall: 0.852

Global (Full):

dir: 0.442

facing: 0.700

pos: 0.553

overall: 0.565

Local:

dir: 1

facing: 1

pos: 0.713

overall: 0.904

Local (Newly):

dir: 1

facing: 1

pos: 0.736

overall: 0.912

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 0.714

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: 0.613

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 8

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• backpack: front, near, facing left
• bike: front-left, slightly far, facing left
• books: front-left, slightly far
Assume your current facing direction is called "north":
• bike is east, near to books.
You have a maximum of 13 exploration steps left.
Strict...

🤔 Assistant Thinking (click to toggle)
I need to reach the yellow door to access/see into another room, but it must be in my current FOV to JumpTo it. From my current facing (east), rotate 180° to face west where I previously saw the yellow door, then observe to bring it back into view (and possibly catch any missing objects)....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Built an integer-coordinate map by choosing the simplest positions consistent with all observed bearing/distance bins across viewpoints and the reported near-object cardinal relations; agent is currently at the red door facing west.
FINAL ANSWER:
{
"agent": { "position": [1, -2], "facing"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "west"
  },
  "red door": {
    "position": [
      1,
      -2
    ]
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ]
  },
  "laptop": {
    "position": [
      -2,
      3
    ],
    "facing": "south"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -4,
      1
    ],
    "facing": "west"
  },
  "backpack": {
    "position": [
      2,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "books": {
    "position": [
      5,
      2
    ]
  },
  "pillow": {
    "position": [
      3,
      2
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      -2
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map using the current FOV only, assigning integer coordinates consistent with the reported relative bearings/distances and the given proximity constraint (desklamp near shoppingcart).
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"desklamp": {
"po...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "desklamp": {
      "position": [
        1,
        4
      ],
      "facing": "-y"
    },
    "shoppingcart": {
      "position": [
        0,
        3
      ],
      "facing": "+x"
    },
    "truck": {
      "position": [
        2,
        4
      ],
      "facing": "+y"
    },
    "yellow door": {
      "position": [
        -2,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "shoppingcart": {
    "position": [
      0,
      4
    ],
    "facing": "north"
  },
  "yellow door": {
    "position": [
      -2,
      2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      2,
      4
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      4,
      6
    ],
    "facing": "west"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "desklamp": {
      "position": [
        1,
        4
      ],
      "facing": "-y"
    },
    "shoppingcart": {
      "position": [
        0,
        3
      ],
      "facing": "+x"
    },
    "truck": {
      "position": [
        2,
        4
      ],
      "facing": "+y"
    },
    "yellow door": {
      "position": [
        -2,
        3
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🧠 Cognitive Map Metrics

Global:

dir: 0.924

facing: 1

pos: 0.888

overall: 0.937

Global (Full):

dir: 0.508

facing: 0.700

pos: 0.651

overall: 0.620

Local:

dir: 0.400

facing: 1

pos: 0.746

overall: 0.715

Local (Newly):

(none)

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 0.714

edge_coverage: 0.293

is_action_fail: False

step: 8

action_counts:

move: 1

rotate: 6

return: 0

observe: 8

term: 0

forced_term: 0

query: 0

information_gain: 0.613

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 9

👤 Environment Observation (click to toggle)
You rotated counterclockwise 180°. You observe:
• desklamp: front-right, slightly far, facing backward
• shoppingcart: front, mid distance, facing right
• truck: front-right, slightly far, facing forward
• yellow door: front-left, mid distance, on left wall
Assume your current facing direction is c...

🤔 Assistant Thinking (click to toggle)
Yellow door is in my current FOV, so I should jump to it to exploit doorway visibility into its connected room(s) and reveal the remaining unseen objects....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Infer approximate integer coordinates consistent with observed bearings/distances, anchoring at initial origin and using JumpTo door positions; include only observed items and facing where given.
FINAL ANSWER:
{
"agent": { "position": [-1, -5], "facing": "west" },
"laptop": { "position":...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -5
    ],
    "facing": "west"
  },
  "laptop": {
    "position": [
      -2,
      3
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      1,
      -2
    ]
  },
  "yellow door": {
    "position": [
      -1,
      -5
    ]
  },
  "desklamp": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "shoppingcart": {
    "position": [
      -4,
      -3
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -5,
      0
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      1
    ]
  },
  "books": {
    "position": [
      4,
      1
    ]
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      1
    ],
    "facing": "west"
  },
  "table": {
    "position": [
      -4,
      -7
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Construct a local (agent-centric) map from current FOV; assign integer coordinates consistent with “front-left/front-right” and “mid distance”, and encode shoppingcart facing “right” as +x.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"shoppingcart": {
"position": [2, 3],
...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

{
  "shoppingcart": {
    "position": [
      2,
      2
    ],
    "facing": "north"
  },
  "table": {
    "position": [
      -2,
      2
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

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

🌫️ Fog Probe Response (click to toggle)
THINK: Candidate points A-F lie in the lower-left open area that remains unvisited/unsensed from current observations, so they mark unexplored regions.
FINAL ANSWER: {
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

###############
#......#......#
#......#......#
#......#......#
#......#......#
#......+......#
#......#......#
#####*#########
##..CEA.#######
##.F...B#######
##D.....#######
##......#######
##......#######
##......#######
###############

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: A, B, E

🧠 Cognitive Map Metrics

Global:

dir: 0.859

facing: 0.286

pos: 0.805

overall: 0.650

Global (Full):

dir: 0.558

facing: 0.200

pos: 0.644

overall: 0.468

Local:

dir: 1

facing: 1

pos: 0.838

overall: 0.946

Local (Newly):

dir: 1

facing: 1

pos: 0.838

overall: 0.946

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.786

edge_coverage: 0.341

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: 0.670

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 10

👤 Environment Observation (click to toggle)
You jumped to yellow door. You observe:
• shoppingcart: front-right, mid distance, facing right
• table: front-left, mid distance.
You have a maximum of 11 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
From the yellow doorway, I should perform a local 360° sweep (rotations + observes) to discover the remaining unseen objects (television, suitcase, chair) and confirm any additional room contents visible through the doorway. Start by rotating 90° clockwise to expose a new sector....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will place all previously observed items on a single global integer grid consistent with the relative bearings/distances and jump-to-door constraints; include the agent at the current (yellow door) location and add facing only where an object's facing was observed.
FINAL ANSWER:
{
"agent...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      1,
      -2
    ]
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ]
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      1
    ],
    "facing": "west"
  },
  "pillow": {
    "position": [
      4,
      2
    ]
  },
  "books": {
    "position": [
      5,
      2
    ]
  },
  "backpack": {
    "position": [
      2,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      5,
      1
    ],
    "facing": "north"
  },
  "table": {
    "position": [
      -4,
      -6
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map for only items currently in FOV, assigning integer coordinates consistent with the reported left/right/front and distance bins; include facing only where given.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"laptop": { "position": [0, 6], "f...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "laptop": {
      "position": [
        0,
        6
      ],
      "facing": "-y"
    },
    "desklamp": {
      "position": [
        -2,
        6
      ],
      "facing": "+x"
    },
    "shoppingcart": {
      "position": [
        -1,
        3
      ],
      "facing": "+y"
    },
    "truck": {
      "position": [
        -3,
        6
      ],
      "facing": "-x"
    },
    "red door": {
      "position": [
        1,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "truck": {
    "position": [
      -4,
      6
    ],
    "facing": "west"
  },
  "red door": {
    "position": [
      2,
      2
    ],
    "facing": "east"
  },
  "laptop": {
    "position": [
      0,
      6
    ],
    "facing": "south"
  },
  "shoppingcart": {
    "position": [
      -2,
      2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -2,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "laptop": {
      "position": [
        0,
        6
      ],
      "facing": "-y"
    },
    "desklamp": {
      "position": [
        -2,
        6
      ],
      "facing": "+x"
    },
    "shoppingcart": {
      "position": [
        -1,
        3
      ],
      "facing": "+y"
    },
    "truck": {
      "position": [
        -3,
        6
      ],
      "facing": "-x"
    },
    "red door": {
      "position": [
        1,
        3
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A-F all lie in the lower-left area separated from the currently observed upper rooms by a wall barrier, indicating they are in an unexplored/unobserved region.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: D, A, E

🧠 Cognitive Map Metrics

Global:

dir: 0.987

facing: 1

pos: 0.892

overall: 0.960

Global (Full):

dir: 0.642

facing: 0.700

pos: 0.714

overall: 0.685

Local:

dir: 0.533

facing: 1

pos: 0.789

overall: 0.774

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.786

edge_coverage: 0.415

is_action_fail: False

step: 10

action_counts:

move: 2

rotate: 7

return: 0

observe: 10

term: 0

forced_term: 0

query: 0

information_gain: 0.691

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 11

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• laptop: front, slightly far, facing backward
• desklamp: front-left, slightly far, facing right
• shoppingcart: front-left, mid distance, facing forward
• truck: front-left, slightly far, facing left
• red door: front-right, mid distance, on right wall
Assu...

🤔 Assistant Thinking (click to toggle)
Continue the sweep from the yellow doorway: rotate another 90° clockwise to check the next 90° sector for the still-unseen objects (television, suitcase, chair)....

💬 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), selecting coordinates that satisfy all observed relative bearings/distances and proximity constraints; include only observed entities and add facing only where explicitly observed.
FINAL ANSWER:
{
"ag...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ]
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ]
  },
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "truck": {
    "position": [
      -4,
      1
    ],
    "facing": "west"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "books": {
    "position": [
      5,
      2
    ]
  },
  "pillow": {
    "position": [
      4,
      2
    ]
  },
  "bike": {
    "position": [
      5,
      1
    ],
    "facing": "north"
  },
  "table": {
    "position": [
      -4,
      -6
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Current FOV contains only the red door; encode its relative position in the agent-centered frame (+y forward, +x right) using an integer approximation consistent with “front-left, mid distance”.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"red door": { "position": [-1, 3] ...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

{
  "red door": {
    "position": [
      -2,
      2
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The agent has only explored/observed the upper rooms and doorway areas; the entire lower region containing candidates A-F remains unobserved.
FINAL ANSWER: {
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: E, C, F

🧠 Cognitive Map Metrics

Global:

dir: 0.987

facing: 1

pos: 0.892

overall: 0.960

Global (Full):

dir: 0.642

facing: 0.700

pos: 0.714

overall: 0.685

Local:

dir: 0

facing: 1

pos: 0.779

overall: 0.593

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.786

edge_coverage: 0.415

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: 0.691

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 12

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• red door: front-left, mid distance, on front wall.
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)
Keep sweeping from the yellow doorway; rotate 90° clockwise again to view the next sector and search for the remaining unseen objects (television, suitcase, chair)....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assemble a single consistent integer-coordinate global map (origin at initial agent pose), using the qualitative bearings/distances to place all observed items and setting object facings by converting their observed egocentric facings into global cardinals.
FINAL ANSWER:
{
"agent": { "posi...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      1,
      -2
    ]
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ]
  },
  "laptop": {
    "position": [
      -2,
      3
    ],
    "facing": "south"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -4,
      1
    ],
    "facing": "west"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      5,
      1
    ],
    "facing": "north"
  },
  "books": {
    "position": [
      5,
      2
    ]
  },
  "pillow": {
    "position": [
      3,
      2
    ]
  },
  "table": {
    "position": [
      -3,
      -6
    ]
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "television": {
    "position": [
      1,
      -9
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map for only the objects currently in FOV, using approximate integer coordinates consistent with the binned bearings/distances; include facing only where specified.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"television": { "position": [-1, 5], "faci...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "television": {
      "position": [
        -1,
        5
      ],
      "facing": "-y"
    },
    "suitcase": {
      "position": [
        0,
        1
      ],
      "facing": "+x"
    },
    "chair": {
      "position": [
        -3,
        5
      ],
      "facing": "-y"
    },
    "table": {
      "position": [
        2,
        2
      ]
    }
  }
}

🎯 Ground Truth

{
  "suitcase": {
    "position": [
      0,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      -2,
      4
    ],
    "facing": "north"
  },
  "television": {
    "position": [
      -2,
      6
    ],
    "facing": "north"
  },
  "table": {
    "position": [
      2,
      2
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "television": {
      "position": [
        -1,
        5
      ],
      "facing": "-y"
    },
    "suitcase": {
      "position": [
        0,
        1
      ],
      "facing": "+x"
    },
    "chair": {
      "position": [
        -3,
        5
      ],
      "facing": "-y"
    },
    "table": {
      "position": [
        2,
        2
      ]
    }
  }
}

🎯 Ground Truth

{
  "suitcase": {
    "position": [
      0,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      -2,
      4
    ],
    "facing": "north"
  },
  "television": {
    "position": [
      -2,
      6
    ],
    "facing": "north"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.958

facing: 1

pos: 0.894

overall: 0.951

Global (Full):

dir: 0.958

facing: 1

pos: 0.894

overall: 0.951

Local:

dir: 0.700

facing: 1

pos: 0.821

overall: 0.840

Local (Newly):

dir: 0.667

facing: 1

pos: 0.796

overall: 0.821

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 1

edge_coverage: 0.488

is_action_fail: False

step: 12

action_counts:

move: 2

rotate: 9

return: 0

observe: 12

term: 0

forced_term: 0

query: 0

information_gain: 0.910

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 13

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

🤔 Assistant Thinking (click to toggle)
All 12 objects (laptop, television, backpack, desklamp, suitcase, bike, shoppingcart, truck, chair, pillow, table, books) and both doors (red door, yellow door) have been observed. To minimize further cost, terminate exploration now....

💬 Assistant Action
Actions: [Term()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Built a globally consistent integer-coordinate map by triangulating from the origin using observed door positions and then placing each object to satisfy all relative bearings/distances and stated proximity relations; set each object's cardinal facing by converting its observed egocentric fac...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -1,
      -5
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      2,
      -3
    ]
  },
  "yellow door": {
    "position": [
      -1,
      -5
    ]
  },
  "laptop": {
    "position": [
      -1,
      3
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      3,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -3
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      1
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -2,
      -3
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -3,
      0
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      3,
      -9
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      4,
      2
    ]
  },
  "table": {
    "position": [
      -2,
      -7
    ]
  },
  "books": {
    "position": [
      5,
      2
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -1,
      2
    ],
    "facing": "south"
  },
  "television": {
    "position": [
      1,
      -10
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -3,
      0
    ],
    "facing": "east"
  },
  "suitcase": {
    "position": [
      -1,
      -6
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      5,
      0
    ],
    "facing": "north"
  },
  "shoppingcart": {
    "position": [
      -3,
      -2
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -5,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -8
    ],
    "facing": "north"
  },
  "pillow": {
    "position": [
      3,
      2
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -3,
      -6
    ],
    "facing": "east"
  },
  "books": {
    "position": [
      5,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      1,
      -2
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      -1,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -1,
      -4
    ],
    "facing": "south"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.800

facing: 1

pos: 0.776

overall: 0.859

Global (Full):

dir: 0.800

facing: 1

pos: 0.776

overall: 0.859

Local:

(none)

Local (Newly):

(none)

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 1

edge_coverage: 0.488

is_action_fail: False

step: 13

action_counts:

move: 2

rotate: 9

return: 0

observe: 12

term: 1

forced_term: 0

query: 0

information_gain: 0.910

reward: -0.100

is_done: True

is_valid_action: True

🔄 FB Turn 14

👤 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 view (previously: laptop front-left, mid distance, facing backward) and detect any immediate changes....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I don’t have enough metric information to recover exact integer coordinates from the binned bearings/distances alone, so I’m outputting a single globally consistent approximate integer layout that includes all objects/doors and known facings.
FINAL ANSWER:
```json
{
"agent": { "position": ...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.318

facing: 0.333

pos: 0.402

overall: 0.351

🧾 False Belief Object Sets

all_objects: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

all_changed_objects: ['backpack', 'bike', 'chair', 'shoppingcart']

newly_observed_changed (this turn): []

unchanged_objects: ['books', 'desklamp', 'laptop', 'pillow', 'red door', 'suitcase', 'table', 'television', 'truck', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

🧭 False Belief Info

step: 1

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'backpack', 'ori': True}, {'name': 'bike', 'ori': True}, {'name': 'shoppingcart', 'pos': True}, {'name': 'chair', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: ['laptop']

🔄 FB Turn 15

👤 Environment Observation (click to toggle)
You observe:
• laptop: front-left, mid distance, facing backward.
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)
Next, check the east-facing sector from the initial position; previously it had no objects in FOV, so any new visibility there would immediately indicate a moved object....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single consistent global JSON map (integer grid) including agent, all objects, and both doors; facings provided where inferable from earlier observations, otherwise omitted.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "north" },
"laptop": { "position": [-2, 2], "...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.864

facing: 0.833

pos: 0.803

overall: 0.833

🧾 False Belief Object Sets

all_objects: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

all_changed_objects: ['backpack', 'bike', 'chair', 'shoppingcart']

newly_observed_changed (this turn): []

unchanged_objects: ['books', 'desklamp', 'laptop', 'pillow', 'red door', 'suitcase', 'table', 'television', 'truck', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

🧭 False Belief Info

step: 2

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'backpack', 'ori': True}, {'name': 'bike', 'ori': True}, {'name': 'shoppingcart', 'pos': True}, {'name': 'chair', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 16

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

🤔 Assistant Thinking (click to toggle)
Rotate to face south from the initial position and observe the doors; any change in their relative bearings/distances would indicate room-layout-adjacent shifts (or moved items now visible near them)....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will output a single globally-referenced JSON map (origin at the agent’s initial position, +y = north) that includes the agent, all 12 objects with inferred integer coordinates and facings where observed, and both doors.
FINAL ANSWER: {
"agent": { "position": [0, 0], "facing": "north" },
...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.924

facing: 0.833

pos: 0.868

overall: 0.875

🧾 False Belief Object Sets

all_objects: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

all_changed_objects: ['backpack', 'bike', 'chair', 'shoppingcart']

newly_observed_changed (this turn): []

unchanged_objects: ['books', 'desklamp', 'laptop', 'pillow', 'red door', 'suitcase', 'table', 'television', 'truck', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

🧭 False Belief Info

step: 3

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'backpack', 'ori': True}, {'name': 'bike', 'ori': True}, {'name': 'shoppingcart', 'pos': True}, {'name': 'chair', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

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

🔄 FB Turn 17

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• red door: front-left, mid distance, on left wall
• yellow door: front-slight-right, slightly far, on front wall.
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)
To detect changes efficiently, revisit the same high-information vantage point as before: the red door. Jumping there (keeping current south-facing orientation) lets me compare the doorway view against the baseline observations taken from that exact pose/orientation....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must output a complete global JSON cognitive map (agent, all objects, and both doors) using the initial position as origin and cardinal facings.
FINAL ANSWER:
{
"agent": { "position": [1, -2], "facing": "south" },
"red door": { "position": [1, -2] },
"yellow door": { "position": [-1,...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.591

facing: 1

pos: 0.649

overall: 0.746

🧾 False Belief Object Sets

all_objects: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

all_changed_objects: ['backpack', 'bike', 'chair', 'shoppingcart']

newly_observed_changed (this turn): []

unchanged_objects: ['books', 'desklamp', 'laptop', 'pillow', 'red door', 'suitcase', 'table', 'television', 'truck', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

🧭 False Belief Info

step: 4

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'backpack', 'ori': True}, {'name': 'bike', 'ori': True}, {'name': 'shoppingcart', 'pos': True}, {'name': 'chair', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 18

👤 Environment Observation (click to toggle)
You jumped to red door. You observe:
• yellow door: front-right, mid distance, on front wall.
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)
Recreate the baseline red-door sweep: rotate 90° clockwise (to match the prior orientation when desklamp/shoppingcart/truck/yellow door were visible) and observe; compare object presence/relative bearings/distances/orientations to detect any changed items....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single consistent global grid map (origin at initial pose) that includes the agent, all objects, and both doors, using cardinal facings where applicable.
FINAL ANSWER:
{
"agent": { "position": [1, -2], "facing": "west" },
"laptop": { "position": [-2, 3], "facing": "south" },
...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: chair:

dir: None

pos: 0.171

facing: None

overall: None

Retention: chair:

dir: None

pos: 0.779

facing: None

overall: None

Unchanged (all):

dir: 0.864

facing: 1

pos: 0.852

overall: 0.905

🧾 False Belief Object Sets

all_objects: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

all_changed_objects: ['backpack', 'bike', 'chair', 'shoppingcart']

newly_observed_changed (this turn): ['chair']

unchanged_objects: ['books', 'desklamp', 'laptop', 'pillow', 'red door', 'suitcase', 'table', 'television', 'truck', 'yellow door']

pred_changed_keys: ['chair']

pred_unchanged_keys: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

🧭 False Belief Info

step: 5

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'backpack', 'ori': True}, {'name': 'bike', 'ori': True}, {'name': 'shoppingcart', 'pos': True}, {'name': 'chair', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['chair']

newly_observed_unchanged_objects: ['desklamp', 'truck']

🔄 FB Turn 19

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• desklamp: front-right, slightly far, facing backward
• truck: front-right, slightly far, facing forward
• chair: front, slightly far, facing right
• yellow door: front-left, mid distance, on left wall.
You have a maximum of 10 exploration steps left.
Strict...

🤔 Assistant Thinking (click to toggle)
Compared to the baseline from this same red-door vantage/sweep, the shoppingcart is missing and a chair is now visible, indicating at least one position change. Continue the same sweep by rotating 90° clockwise to the sector that previously showed laptop/pillow/books, and compare....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct an approximate global map consistent with observed relative bearings/distances; include agent, all objects, and both doors with cardinal facings.
FINAL ANSWER:
{
"agent": { "position": [1, -2], "facing": "north" },
"red door": { "position": [1, -2] },
"yellow door": { "positio...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.682

facing: 0.667

pos: 0.587

overall: 0.645

🧾 False Belief Object Sets

all_objects: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

all_changed_objects: ['backpack', 'bike', 'chair', 'shoppingcart']

newly_observed_changed (this turn): []

unchanged_objects: ['books', 'desklamp', 'laptop', 'pillow', 'red door', 'suitcase', 'table', 'television', 'truck', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

🧭 False Belief Info

step: 6

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'backpack', 'ori': True}, {'name': 'bike', 'ori': True}, {'name': 'shoppingcart', 'pos': True}, {'name': 'chair', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: ['books', 'pillow']

🔄 FB Turn 20

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• laptop: front-left, slightly far, facing backward
• pillow: front-right, slightly far
• books: front-right, slightly far
Assume your current facing direction is called "north":
• pillow is west, near to books.
You have a maximum of 9 exploration steps left....

🤔 Assistant Thinking (click to toggle)
So far, relative to the baseline at the same red-door vantage, the shoppingcart is no longer in view and a chair is now in view—strong evidence of at least one position change. Continue the same sweep: rotate 90° clockwise to the sector that previously showed backpack/bike/books and compare....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will provide a single consistent global JSON map (integer coordinates) including the agent, all objects, and both doors, using the initial position as origin and cardinal facings.
FINAL ANSWER:
```json
{
"agent": { "position": [2, -3], "facing": "east" },
"red door": { "position": [2, ...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: backpack:

dir: None

pos: None

facing: 1

overall: None

Retention: backpack:

dir: None

pos: None

facing: 0

overall: None

Changed: bike:

dir: None

pos: None

facing: 1

overall: None

Retention: bike:

dir: None

pos: None

facing: 0

overall: None

Unchanged (all):

dir: 0.545

facing: 0.667

pos: 0.615

overall: 0.609

🧾 False Belief Object Sets

all_objects: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

all_changed_objects: ['backpack', 'bike', 'chair', 'shoppingcart']

newly_observed_changed (this turn): ['backpack', 'bike']

unchanged_objects: ['books', 'desklamp', 'laptop', 'pillow', 'red door', 'suitcase', 'table', 'television', 'truck', 'yellow door']

pred_changed_keys: ['backpack', 'bike']

pred_unchanged_keys: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

🧭 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': 'backpack', 'ori': True}, {'name': 'bike', 'ori': True}, {'name': 'shoppingcart', 'pos': True}, {'name': 'chair', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['backpack', 'bike']

newly_observed_unchanged_objects: []

🔄 FB Turn 21

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• backpack: front, near, facing backward
• bike: front-left, slightly far, facing forward
• books: front-left, slightly far
Assume your current facing direction is called "north":
• bike is east, near to books.
You have a maximum of 8 exploration steps left.
...

🤔 Assistant Thinking (click to toggle)
We likely already have 4 changes (shoppingcart moved; chair moved; backpack rotated; bike rotated), but I should confirm by revisiting the yellow door doorway view where shoppingcart/table were previously visible. From current red-door position facing east, rotate 180° to face west (yellow door shou...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a single global JSON map with integer coordinates (origin at initial agent position), including agent, all 12 objects, and both doors; include facing only where applicable. Using the latest observations plus prior sweep consistency, output a best-fit coordinate assignment and current fac...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.848

facing: 1

pos: 0.788

overall: 0.879

🧾 False Belief Object Sets

all_objects: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

all_changed_objects: ['backpack', 'bike', 'chair', 'shoppingcart']

newly_observed_changed (this turn): []

unchanged_objects: ['books', 'desklamp', 'laptop', 'pillow', 'red door', 'suitcase', 'table', 'television', 'truck', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['backpack', 'bike', 'books', 'chair', 'desklamp', 'laptop', 'pillow', 'red door', 'shoppingcart', 'suitcase', 'table', 'television', 'truck', 'yellow door']

🧭 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': 'backpack', 'ori': True}, {'name': 'bike', 'ori': True}, {'name': 'shoppingcart', 'pos': True}, {'name': 'chair', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: ['table']

🔄 FB Turn 22

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You jumped to yellow door. You observe:
• chair: front-right, mid distance, facing right
• table: 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)
I have evidence for exactly four changes: (1) shoppingcart is no longer seen from the same red-door viewpoint where it previously was, and it’s also missing from the yellow-door viewpoint where it previously was → shoppingcart position change. (2) chair newly appears from the red-door viewpoint and ...

💬 Assistant Action
Actions: [Term(changes="shoppingcart: position, chair: position, backpack: orientation, bike: orientation")]

📈 Turn Metrics

reward: 1

is_done: True

is_valid_action: True

success: 1

🧭 False Belief Info

step: 9

correctly_identified_changes: 1

f1_overall: 1

f1_position: 1

f1_facing: 1

action_cost: 8

action_cost_after_seen: None

ground_truth_changes: [{'name': 'backpack', 'ori': True}, {'name': 'bike', 'ori': True}, {'name': 'shoppingcart', 'pos': True}, {'name': 'chair', 'pos': True}]

reported_changes: [{'name': 'shoppingcart', 'pos': True}, {'name': 'chair', 'pos': True}, {'name': 'backpack', 'ori': True}, {'name': 'bike', 'ori': True}]

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

📊 Task: DirectionEvaluationTask

Question 1 (ID: c9de6790a897c673)

❓ 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 desklamp.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Truck and desklamp were repeatedly observed in the same sector and at similar range; truck appears slightly farther to the right than the desklamp, implying it lies to the east of it at close range.
FINAL ANSWER:
east, near...

✅ Evaluation Results

id: c9de6790a897c673

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

action: None

answer: north west, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Evaluation state — Q1: DirectionEvaluationTask

Question 2 (ID: 7360e44dad33a0d5)

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

🤔 Assistant Answer (click to toggle)
THINK:
Infer approximate coordinates from the observed bearings/distances at the yellow-door viewpoint; books end up mostly above (north of) chair with a separation in the 8–16 range.
FINAL ANSWER:
north, far...

✅ Evaluation Results

id: 7360e44dad33a0d5

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

Question 3 (ID: 50faba75f48ab8cb)

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

🤔 Assistant Answer (click to toggle)
THINK:
Backpack is east of the red door, while the table is further to the south-west (via the yellow door), so backpack ends up north-east of the table at a moderately large separation.
FINAL ANSWER:
north-east, slightly far...

✅ Evaluation Results

id: 50faba75f48ab8cb

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

action: None

answer: north east, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: PovEvaluationTask

Question 1 (ID: fffaede931ebf4f3)

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

🤔 Assistant Answer (click to toggle)
THINK:
From the yellow-door viewpoint, desklamp was to the left of laptop at a comparable range, implying it lies roughly to the west of the laptop. Since the laptop faces south, west is to the agent’s right (outside the ±45° front bins), so it’s beyond the front FOV. The separation is best matched ...

✅ Evaluation Results

id: fffaede931ebf4f3

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

evaluation_mode: default

Question 2 (ID: b8cd5a53479d1c3e)

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

🤔 Assistant Answer (click to toggle)
THINK:
From the red door view, backpack was closer straight ahead while books were farther and to the front-left. Moving to backpack and facing backpack’s own direction makes books fall into the front-right region at a moderate separation.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: b8cd5a53479d1c3e

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

action: None

answer: front-right, slightly far

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 3 (ID: a67a1cef66054f66)

❓ Evaluation Question (click to toggle)
Now you jump to desklamp'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 the desklamp’s position, facing the desklamp’s facing direction, the laptop lies ahead and to the left at a moderate separation.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: a67a1cef66054f66

question: Now you jump to desklamp'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-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 5902e70da1b23e88)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that laptop is front-left, mid distance, facing right; red 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:
Facing the object’s own direction, “laptop facing right” implies the laptop faces south, matching earlier evidence. The only object previously inferred to face east (so red door can be “on front wall”) is the desklamp, and from it the laptop can appear front-left while the red door is front-r...

✅ Evaluation Results

id: 5902e70da1b23e88

question: Now you jump to an object's position, facing its direction. You observe that laptop is front-left, mid distance, facing right; red 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: desklamp

final_pos: [3, 11]

final_ori: [1, 0]

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

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

Question 2 (ID: 7f5c758512898b6b)

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

🤔 Assistant Answer (click to toggle)
THINK:
From earlier observations, the shoppingcart’s absolute facing is north. In the question it appears “facing backward,” so my current facing must be south. The object with absolute facing south (from prior observations) is the laptop, so I’m standing at the laptop.
FINAL ANSWER:
laptop...

✅ Evaluation Results

id: 7f5c758512898b6b

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

action: None

answer:

answer: laptop

final_pos: [5, 13]

final_ori: [0, -1]

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

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

Question 3 (ID: 7f4f9f9c8d6112b9)

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

🤔 Assistant Answer (click to toggle)
THINK:
Bike is facing forward, and previously bike’s facing direction aligns with global north; so I’m also facing north. The object I’m standing on must therefore face north as well. Among observed objects with north-facing orientation, the spatial layout best matches standing at the backpack (plac...

✅ Evaluation Results

id: 7f4f9f9c8d6112b9

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

action: None

answer:

answer: backpack

final_pos: [9, 9]

final_ori: [0, 1]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'pillow', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'books', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 3

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 286b5d2b65475d4f)

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

After executing the actions, what is the ego relation o...

🤔 Assistant Answer (click to toggle)
THINK:
After returning to start (0,0) facing north: Rotate(-90) → face west, so the “front, mid distance” object is the desklamp at (-3,0). Then Rotate(-90) → face south; from desklamp the “front-left, slightly far” object is the yellow door at (-1,-4). Rotate(-180) → face north. Laptop is at (-1,2)...

✅ Evaluation Results

id: 286b5d2b65475d4f

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front, mid distance. 3. Rotate(-90) 4. Jump to the object at front-left, slightly far. 5. 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, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: e413d4447d27b08e)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
From start facing south, the “front-slight-right, slightly far” object is the yellow door. From the yellow door facing south, the “front-left, slightly far” object is the chair. From chair, suitcase lies to the northwest at ~5 units, so relative to facing north it is front-left and slightly f...

✅ Evaluation Results

id: e413d4447d27b08e

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

action: None

answer: front-left, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 3 (ID: 45597bafd7e0cdbc)

❓ 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-left, mid distance.
3. Rotate(90)
4. Jump to the object at front-right, slightly far.
5. Rotate(-180)

After executing the actions, what is the ego relat...

🤔 Assistant Answer (click to toggle)
THINK:
After the given action sequence, the agent ends at the red door facing south. From the red door, the desklamp lies behind-right (northwest), which falls outside the ±45° ego angle bins, and its distance remains in the 4–8 range.
FINAL ANSWER:
beyond-fov, slightly far...

✅ Evaluation Results

id: 45597bafd7e0cdbc

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-left, mid distance. 3. Rotate(90) 4. Jump to the object at front-right, slightly far. 5. Rotate(-180) After executing the actions, what is the ego relation of desklamp relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 5ed57112b3bfdc2e)

❓ 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:
laptop is at front-left, slightly far, facing backward; pillow is at front-right, slightly far; books is at front-right, sligh...

🤔 Assistant Answer (click to toggle)
THINK:
From the start (facing north), rotate to face south so the red door is in FOV, jump to the red door (orientation unchanged), then rotate to face north to match the final view.
FINAL ANSWER:
Rotate(180), JumpTo(red door), Rotate(180)...

✅ Evaluation Results

id: 5ed57112b3bfdc2e

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: laptop is at front-left, slightly far, facing backward; pillow is at front-right, slightly far; books is at front-right, slightly far 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, 9]

final_ori: [0, 1]

room_id: [1, 3]

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

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

final_observation: [{'name': 'laptop', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'pillow', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'books', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [7.0, 9.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: default

Question 2 (ID: 1f898b79fbf64cc4)

❓ 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:
shoppingcart is at front-left, mid distance, facing forward; laptop is at front, slightly far, facing backward; desklamp is at...

🤔 Assistant Answer (click to toggle)
THINK:
To get that view, end up near the table inside the yellow-door room and face north; from start, rotate to bring the yellow door into FOV, jump there, orient to see the table, jump to it, then face north.
FINAL ANSWER:
Rotate(180), JumpTo(yellow door), Rotate(90), JumpTo(table), Rotate(90)...

✅ Evaluation Results

id: 1f898b79fbf64cc4

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: shoppingcart is at front-left, mid distance, facing forward; laptop is at front, slightly far, facing backward; desklamp is at front-left, slightly far, 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: [5, 7]

final_ori: [0, 1]

room_id: [2, 1]

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

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

final_observation: [{'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'laptop', 'direction': 'front', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'desklamp', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [3.0, 5.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: default

Question 3 (ID: 8a8f0522e5b332f6)

❓ 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:
backpack is at front, mid distance, facing backward; bike is at front-left, mid distance, facing backward; red door is at fron...

🤔 Assistant Answer (click to toggle)
THINK:
From start, face south to bring the red door into view, jump to it, then face north to bring pillow into view, jump to pillow, and finally face south so the red door appears front-right while backpack/bike are in front sectors.
FINAL ANSWER:
Rotate(180), JumpTo(red door), Rotate(180), JumpTo(...

✅ Evaluation Results

id: 8a8f0522e5b332f6

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: backpack is at front, mid distance, facing backward; bike is at front-left, mid distance, facing backward; red 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: [9, 13]

final_ori: [0, -1]

room_id: 3

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'red door'], ['rotate', 180], ['jumpto', 'pillow'], ['rotate', 180]]

final_observation: [{'name': 'backpack', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'bike', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'red 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: [9.0, 13.0]

final_ori: [0, -1]

visible_match: True

score: 1

evaluation_mode: default

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 2fec3ac651cf4a7c)

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

🤔 Assistant Answer (click to toggle)
THINK:
Infer door locations consistent with all angle/distance bins, then solve object integer coordinates that match multiple viewpoint constraints (notably laptop aligned with yellow door “front”).
FINAL ANSWER:
(-4, 1); (-3, 0); (2, -8); (-1, 2)...

✅ Evaluation Results

id: 2fec3ac651cf4a7c

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

action: None

answer: [[-5, 2], [-3, 0], [1, -8], [-1, 2]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.923

threshold: 0.900

score: 0.923

evaluation_mode: default

Question 2 (ID: 237ae2871d72362b)

❓ 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: backpack, chair, bike.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Infer door coordinates first from consistent bearings/distances across multiple viewpoints, then place the requested objects using the egocentric bearings/distances plus the reported near-proximity constraints.
FINAL ANSWER:
(2, -2); (1, -8); (5, 0)...

✅ Evaluation Results

id: 237ae2871d72362b

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

action: None

answer: [[3, -2], [1, -8], [5, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.953

threshold: 0.900

score: 0.953

evaluation_mode: default

Question 3 (ID: 0ca18bd6fca57b5f)

❓ 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: table, backpack, bike, books.
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 solving consistent door/object placements from binned bearings/distances and cross-view constraints (notably shoppingcart alignment), then place table from yellow-door + suitcase relation, and books/bike from red-door dual-bearings + bike-books proximity; backpack...

✅ Evaluation Results

id: 0ca18bd6fca57b5f

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

action: None

answer: [[-3, -6], [3, -2], [5, 0], [5, 2]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.937

threshold: 0.900

score: 0.937

evaluation_mode: default

📊 Task: RotEvaluationTask

Question 1 (ID: 4831d162b78edf8c)

❓ 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: chair, desklamp, laptop, table.
List...

🤔 Assistant Answer (click to toggle)
THINK:
As I rotate counterclockwise from facing north, objects come directly ahead in order of decreasing bearing from 0° down to -180°, then continuing past -180° (equivalently +180°) back toward 0°. From the explored layout, laptop is northwest of start (small negative bearing), desklamp is furthe...

✅ Evaluation Results

id: 4831d162b78edf8c

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: chair, desklamp, laptop, table. 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: ['laptop', 'desklamp', 'table', 'chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: b3bb94ba48268066)

❓ 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 clockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: backpack, bike, chair, pillow, shoppingcart...

🤔 Assistant Answer (click to toggle)
THINK:
Determine each target object’s bearing from the start (0,0) facing north, then order by increasing clockwise bearing (wrapping past 180°); use nearer-first only if bearings tie.
FINAL ANSWER:
pillow, bike, backpack, chair, shoppingcart...

✅ Evaluation Results

id: b3bb94ba48268066

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning clockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: backpack, bike, chair, pillow, 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: ['pillow', 'bike', 'backpack', 'chair', 'shoppingcart']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: a73f3abfbc74d987)

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

🤔 Assistant Answer (click to toggle)
THINK:
Determine each object’s bearing from the start (0,0) facing north, then order them as the facing direction sweeps counterclockwise: 0° → -180° → +180° → back to 0°. The bearings place laptop first (slightly left of north), then truck (west), then table (southwest), then television (due south)...

✅ Evaluation Results

id: a73f3abfbc74d987

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, table, television, 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: ['laptop', 'truck', 'table', 'television']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: RotDualEvaluationTask

Question 1 (ID: ac2a6bf257762996)

❓ 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:
laptop, desklamp, table, cha...

🤔 Assistant Answer (click to toggle)
THINK:
Since the laptop is initially front-left (northwest of the start), it would be encountered early only if the rotation swept from north toward west, i.e., counterclockwise. The subsequent order (desklamp → table → chair) continues around toward southwest/south, consistent with counterclockwise...

✅ Evaluation Results

id: ac2a6bf257762996

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: laptop, desklamp, table, 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: fabe192c1f42a07a)

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

🤔 Assistant Answer (click to toggle)
THINK:
The front-appearance order matches increasing bearings from near-northeast through east, southeast, south, to southwest, which corresponds to a clockwise sweep from the initial north-facing direction.
FINAL ANSWER:
clockwise...

✅ Evaluation Results

id: fabe192c1f42a07a

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

action: None

answer: clockwise

task_type: RotDualEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 1fe9e0ec40250bfb)

❓ 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:
laptop, truck, table, televi...

🤔 Assistant Answer (click to toggle)
THINK:
From facing north, a clockwise rotation would first bring objects on the right (east) side into view; an object to the left (west) of north would appear near the end. Since the laptop appeared first (previously observed front-left from north), the rotation must have started toward the left.
F...

✅ Evaluation Results

id: 1fe9e0ec40250bfb

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: laptop, truck, table, television 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: c98da85995bf0909)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
From (0, 1) facing west, the truck is directly ahead (west) at about 5 units away, which falls in the slightly far bin.
FINAL ANSWER:
front, slightly far...

✅ Evaluation Results

id: c98da85995bf0909

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

action: None

answer: front-slight-right, slightly far

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 2 (ID: 233021678b15a057)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (2, 1) and face north.
What is the egocentric relation of pillow?

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

🤔 Assistant Answer (click to toggle)
THINK:
From the red door, pillow lies in the front-right quadrant at slightly-far range; from (2,1) facing north, the vector to pillow remains front-right and falls in the mid-distance bin.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 233021678b15a057

question: Treat the red door as the new 'origin' (0, 0). You move to (2, 1) and face north. What is the egocentric relation of pillow? 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

Question 3 (ID: 5807e77e81650360)

❓ Evaluation Question (click to toggle)
Treat the yellow door as the new 'origin' (0, 0).
You move to (-2, -4) and face east.
What is the egocentric relation of television?

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

🤔 Assistant Answer (click to toggle)
THINK:
Using the mapped layout with yellow door as (0,0), television lies to the southeast; from (-2,-4) facing east it is mostly ahead and slightly to the right at a bit over 4m.
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: 5807e77e81650360

question: Treat the yellow door as the new 'origin' (0, 0). You move to (-2, -4) and face east. What is the egocentric relation of television? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 3ba4cbab06925eb6)

❓ Evaluation Question (click to toggle)
You move to a new location and face west.
You observe: truck is at front-slight-right, slightly far, facing forward; shoppingcart is at front-left, slightly far, facing right; desklamp is at front-slight-left, mid distance, facing backward
Still treat your initial position as origin (0, 0)
What is y...

🤔 Assistant Answer (click to toggle)
THINK:
Using previously inferred door positions, I solved object coordinates: shoppingcart (-3,-2), desklamp (-3,0), truck (-4,3). Matching the new west-facing bearings/distances uniquely pins the agent at (0,1).
FINAL ANSWER:
(0, 1)...

✅ Evaluation Results

id: 3ba4cbab06925eb6

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

action: None

answer:

coord: [0, 1]

final_pos: [6, 12]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

final_observation: [{'name': 'truck', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'desklamp', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing backward'}]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: default

Question 2 (ID: 079f24d0e9023ee5)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: pillow is at front, mid distance; books is at front-right, mid distance
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:
Infer the red door’s location in the original frame from the initial observation (facing south): red door was front-left at mid distance, consistent with a vector (1, -2). Thus, with red door as origin, the initial position is (-1, 2). The “new location” corresponds to that position.
FINAL AN...

✅ Evaluation Results

id: 079f24d0e9023ee5

question: You move to a new location and face north. You observe: pillow is at front, mid distance; books is at front-right, mid distance 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: [2, 1]

final_pos: [9, 10]

final_ori: [0, 1]

room_id: 3

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

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

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.243

raw_score: 0.243

score: 0.243

evaluation_mode: default

Question 3 (ID: db6d367fcaa0e174)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: television is at front-right, slightly far, facing left; suitcase is at front-left, mid distance, facing backward; chair is at front, mid distance, facing left
Treat the yellow door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, ...

🤔 Assistant Answer (click to toggle)
THINK:
Use the yellow door as (0,0). From earlier doorway observations, a consistent integer placement is: suitcase at (0,-2), chair at (2,-4), television at (2,-6) (television is 2 units “north/forward” of chair and near). From the new view facing east, the relative vectors match the reported bins ...

✅ Evaluation Results

id: db6d367fcaa0e174

question: You move to a new location and face east. You observe: television is at front-right, slightly far, facing left; suitcase is at front-left, mid distance, facing backward; chair is at front, mid distance, facing left Treat the yellow 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: [-2, -4]

final_pos: [3, 3]

final_ori: [1, 0]

room_id: 2

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

final_observation: [{'name': 'television', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'suitcase', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'chair', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: default

📊 Task: DirectionEvaluationTask

Question 1 (ID: c9de6790a897c673)

❓ 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 desklamp.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll assign a consistent set of integer coordinates (origin at the agent’s initial start, +y = north) that best fits the observed relative bearings/distances across viewpoints. Then compute the truck’s vector from the desklamp on this top-down (global) map to output a cardinal direction and b...

✅ Evaluation Results

id: c9de6790a897c673

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

action: None

answer: north west, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 2 (ID: 7360e44dad33a0d5)

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

🤔 Assistant Answer (click to toggle)
THINK:
I need a single globally-consistent integer-coordinate placement (origin at the initial agent position, +y = north) that satisfies the observed qualitative bearings/distances and the given proximity relations. With no Query() steps taken, exact coordinates are underdetermined, so I choose one...

✅ Evaluation Results

id: 7360e44dad33a0d5

question: You return to your starting position and face north. From a Top-Down map, describe where books is relative to 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: 50faba75f48ab8cb)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compile a single globally-referenced coordinate map consistent with the observed relative bearings/distances (no Query was used, so coordinates are the simplest integer layout fitting the observations). Then compute backpack’s direction/distance from table using their coordinates.
FINAL ANSWE...

✅ Evaluation Results

id: 50faba75f48ab8cb

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

action: None

answer: north east, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: fffaede931ebf4f3)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a globally consistent integer-coordinate map anchored at the initial pose using the observed relative directions/distances and the known jump locations (doors). Then, for the query, place the agent at the laptop and set its facing to the laptop’s facing (south) to compute desklamp’s...

✅ Evaluation Results

id: fffaede931ebf4f3

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

Question 2 (ID: b8cd5a53479d1c3e)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll construct a single global integer-coordinate map consistent with all observed egocentric relations (using the initial pose as origin and +y as north). Then, after “jumping to backpack” and aligning to backpack’s facing (north), I’ll compute books’ egocentric direction and distance bin fr...

✅ Evaluation Results

id: b8cd5a53479d1c3e

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

action: None

answer: front-right, slightly far

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: a67a1cef66054f66)

❓ Evaluation Question (click to toggle)
Now you jump to desklamp'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’ll provide an approximate integer-coordinate global map consistent with the observed relative bearings/distances (using near≈1–2, mid≈3, slightly-far≈5–8). Then, placing the agent at the desklamp and facing the desklamp’s facing (east/right), I’ll compute laptop’s egocentric bearing and dis...

✅ Evaluation Results

id: a67a1cef66054f66

question: Now you jump to desklamp'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-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 5902e70da1b23e88)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that laptop is front-left, mid distance, facing right; red 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:
Assemble a single globally consistent integer-coordinate map that satisfies the observed bearings/distances and the given near-relations, then identify which object’s position+facing (when stood on it) would make the laptop appear front-left (mid) and the red door front-right (slightly far), ...

✅ Evaluation Results

id: 5902e70da1b23e88

question: Now you jump to an object's position, facing its direction. You observe that laptop is front-left, mid distance, facing right; red 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: desklamp

final_pos: [3, 11]

final_ori: [1, 0]

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

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 7f5c758512898b6b)

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

🤔 Assistant Answer (click to toggle)
THINK:
I need to output an integer-grid global cognitive map using the initial agent pose as origin. Since no Query() actions were used, exact coordinates are underdetermined; I’m providing one consistent integer placement that matches the observed left/right/front relationships, distance bins, and ...

✅ Evaluation Results

id: 7f5c758512898b6b

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

action: None

answer:

answer: laptop

final_pos: [5, 13]

final_ori: [0, -1]

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

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 7f4f9f9c8d6112b9)

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

🤔 Assistant Answer (click to toggle)
THINK:
I must output an integer-coordinate global cognitive map using the initial pose as origin. Since no Query() actions were used, exact coordinates are underdetermined; I’ll provide a single self-consistent integer layout that matches the qualitative bearings/distances and the “near” constraints...

✅ Evaluation Results

id: 7f4f9f9c8d6112b9

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

action: None

answer:

answer: backpack

final_pos: [9, 9]

final_ori: [0, 1]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'pillow', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'books', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 3

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 286b5d2b65475d4f)

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

After executing the actions, what is the ego relation o...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll construct a single global coordinate map (origin at the start, +y = initial north) consistent with the observed egocentric relations and the few explicit proximity constraints. Then I’ll simulate the given action sequence from the start and compute laptop’s final egocentric direction/dis...

✅ Evaluation Results

id: 286b5d2b65475d4f

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front, mid distance. 3. Rotate(-90) 4. Jump to the object at front-left, slightly far. 5. 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, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 2 (ID: e413d4447d27b08e)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent global integer-coordinate map (origin at start, +y north) that satisfies all observed egocentric bearings/distances and the reported near-relations. Then simulate the given action sequence from the start: it uniquely targets the yellow door, then the chair, endin...

✅ Evaluation Results

id: e413d4447d27b08e

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

action: None

answer: front-left, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 45597bafd7e0cdbc)

❓ 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-left, mid distance.
3. Rotate(90)
4. Jump to the object at front-right, slightly far.
5. Rotate(-180)

After executing the actions, what is the ego relat...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll provide one concrete integer-coordinate placement consistent with the key observed relations (notably laptop NW of start; doors south; clusters like suitcase near table and chair near television). Then I’ll simulate the given action sequence on this map to determine the final ego relatio...

✅ Evaluation Results

id: 45597bafd7e0cdbc

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-left, mid distance. 3. Rotate(90) 4. Jump to the object at front-right, slightly far. 5. Rotate(-180) After executing the actions, what is the ego relation of desklamp relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 5ed57112b3bfdc2e)

❓ 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:
laptop is at front-left, slightly far, facing backward; pillow is at front-right, slightly far; books is at front-right, sligh...

🤔 Assistant Answer (click to toggle)
THINK:
As no exact coordinates were queried, I’m providing one internally consistent integer-coordinate layout that matches the observed bearings/distances and door connectivity. The final view (laptop front-left; pillow/books front-right) matches being at the red door while facing north, reachable ...

✅ Evaluation Results

id: 5ed57112b3bfdc2e

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: laptop is at front-left, slightly far, facing backward; pillow is at front-right, slightly far; books is at front-right, slightly far 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, 9]

final_ori: [0, 1]

room_id: [1, 3]

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

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

final_observation: [{'name': 'laptop', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'pillow', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'books', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [7.0, 9.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 1f898b79fbf64cc4)

❓ 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:
shoppingcart is at front-left, mid distance, facing forward; laptop is at front, slightly far, facing backward; desklamp is at...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll place all observed items/doors on an integer grid consistent with the relative bearings/distances we saw, using the start as (0,0). For the action-sequence question, the described view matches being at the yellow door facing north, so I’ll give a minimal Rotate/JumpTo/Rotate sequence fro...

✅ Evaluation Results

id: 1f898b79fbf64cc4

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: shoppingcart is at front-left, mid distance, facing forward; laptop is at front, slightly far, facing backward; desklamp is at front-left, slightly far, 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: [5, 7]

final_ori: [0, 1]

room_id: [2, 1]

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

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

final_observation: [{'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'laptop', 'direction': 'front', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'desklamp', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [5.0, 7.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 8a8f0522e5b332f6)

❓ 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:
backpack is at front, mid distance, facing backward; bike is at front-left, mid distance, facing backward; red door is at fron...

🤔 Assistant Answer (click to toggle)
THINK:
Construct a globally consistent integer-coordinate map (origin at start, +y north) that satisfies all observed relative bearings/distances and consistent object facings across multiple viewpoints. For the action sequence, start at origin facing north, rotate to bring the red door into view, j...

✅ Evaluation Results

id: 8a8f0522e5b332f6

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: backpack is at front, mid distance, facing backward; bike is at front-left, mid distance, facing backward; red 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: [9, 13]

final_ori: [0, -1]

room_id: 3

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'red door'], ['rotate', 180], ['jumpto', 'pillow'], ['rotate', 180]]

final_observation: [{'name': 'backpack', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'bike', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'red 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: [11.0, 13.0]

final_ori: [0, -1]

visible_match: False

score: 0

evaluation_mode: prompt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 2fec3ac651cf4a7c)

❓ 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, desklamp, chair, 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 qualitative observations (relative bearings/distances, doorway jumps, and door-to-door/door-to-object constraints) to assign a single consistent set of integer coordinates for all observed items in the global frame where start=(0,0) and north is +y. Then output requested object coordi...

✅ Evaluation Results

id: 2fec3ac651cf4a7c

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

action: None

answer: [[-5, 2], [-3, 0], [1, -8], [-1, 2]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.632

threshold: 0.900

score: 0.632

evaluation_mode: prompt_cogmap

Question 2 (ID: 237ae2871d72362b)

❓ 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: backpack, chair, bike.
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 are underdetermined; I’ll provide one consistent integer-coordinate global map that satisfies all observed left/right/front relations, near constraints (≤2), and the sequence of JumpTo(red door) and JumpTo(yellow door). Then I’ll report the requeste...

✅ Evaluation Results

id: 237ae2871d72362b

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

action: None

answer: [[3, -2], [1, -8], [5, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.935

threshold: 0.900

score: 0.935

evaluation_mode: prompt_cogmap

Question 3 (ID: 0ca18bd6fca57b5f)

❓ 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: table, backpack, bike, books.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
I must output a single global (allocentric) cognitive map in JSON with integer coordinates, using the initial agent pose as origin (0,0) facing north. Since no Query() actions were used, exact metric coordinates were never directly revealed; I will provide a single internally-consistent integ...

✅ Evaluation Results

id: 0ca18bd6fca57b5f

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

action: None

answer: [[-3, -6], [3, -2], [5, 0], [5, 2]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.914

threshold: 0.900

score: 0.914

evaluation_mode: prompt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 4831d162b78edf8c)

❓ 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: chair, desklamp, laptop, table.
List...

🤔 Assistant Answer (click to toggle)
THINK:
We never used Query(), so exact coordinates are underdetermined from binned bearings/distances; I’ll provide one integer-coordinate global layout consistent with all observations up to bin-level (doors/objects placed to satisfy the relative bearings, distance bins, and given proximity constra...

✅ Evaluation Results

id: 4831d162b78edf8c

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: chair, desklamp, laptop, table. 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: ['laptop', 'desklamp', 'table', 'chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: b3bb94ba48268066)

❓ 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 clockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: backpack, bike, chair, pillow, shoppingcart...

🤔 Assistant Answer (click to toggle)
THINK:
I must produce a global JSON cognitive map using the initial agent position as origin and cardinal facing. Since no Query() coordinates were taken during exploration, I’ll provide a single internally consistent integer-grid placement that satisfies the observed relative bearings/proximities (...

✅ Evaluation Results

id: b3bb94ba48268066

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning clockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: backpack, bike, chair, pillow, 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: ['pillow', 'bike', 'backpack', 'chair', 'shoppingcart']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: a73f3abfbc74d987)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single globally consistent set of integer coordinates (relative to the initial origin) that best fits the observed egocentric bearings/distances and the doorway jumps; then compute each target object’s global bearing from the origin and sort by the order encountered when rotating ...

✅ Evaluation Results

id: a73f3abfbc74d987

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, table, television, 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: ['laptop', 'truck', 'table', 'television']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: c98da85995bf0909)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
As no Query actions were used, I assign a single consistent set of integer coordinates that best fits all observed relative bearings/distances across viewpoints (origin at initial agent pose, +y north). Then compute truck’s egocentric direction/distance from the hypothetical new pose (0,1) fa...

✅ Evaluation Results

id: c98da85995bf0909

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

action: None

answer: front-slight-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 2 (ID: 233021678b15a057)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (2, 1) and face north.
What is the egocentric relation of pillow?

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll provide one consistent integer-coordinate layout that satisfies the observed relative directions/distances and proximity constraints, anchored at the initial agent origin. Then I’ll re-express pillow relative to the red door frame and compute its egocentric direction/distance from the qu...

✅ Evaluation Results

id: 233021678b15a057

question: Treat the red door as the new 'origin' (0, 0). You move to (2, 1) and face north. What is the egocentric relation of pillow? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 5807e77e81650360)

❓ Evaluation Question (click to toggle)
Treat the yellow door as the new 'origin' (0, 0).
You move to (-2, -4) and face east.
What is the egocentric relation of television?

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

🤔 Assistant Answer (click to toggle)
THINK:
As no Query() was used, I assign a single consistent integer-grid layout that matches all observed relative bearings/distances and proximity constraints as closely as possible. Then I compute the television’s position relative to the new agent pose in the yellow-door-centered frame to get its...

✅ Evaluation Results

id: 5807e77e81650360

question: Treat the yellow door as the new 'origin' (0, 0). You move to (-2, -4) and face east. What is the egocentric relation of television? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 3ba4cbab06925eb6)

❓ Evaluation Question (click to toggle)
You move to a new location and face west.
You observe: truck is at front-slight-right, slightly far, facing forward; shoppingcart is at front-left, slightly far, facing right; desklamp is at front-slight-left, mid distance, facing backward
Still treat your initial position as origin (0, 0)
What is y...

🤔 Assistant Answer (click to toggle)
THINK:
I reconstruct a consistent global integer-coordinate map by chaining the relative observations across rotations and doorway jumps, anchoring the initial agent pose at (0,0) facing north. Then I solve the final localization query by finding the unique integer (x,y) (facing west) whose relative...

✅ Evaluation Results

id: 3ba4cbab06925eb6

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

action: None

answer:

coord: [0, 1]

final_pos: [6, 12]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

final_observation: [{'name': 'truck', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'desklamp', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing backward'}]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.368

raw_score: 0.368

score: 0.368

evaluation_mode: prompt_cogmap

Question 2 (ID: 079f24d0e9023ee5)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: pillow is at front, mid distance; books is at front-right, mid distance
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:
Construct a single consistent global placement (integer grid) that matches the observed qualitative bearings/distances and JumpTo door transitions; then localize the new agent pose using the known (red-door–relative) positions of pillow and books and the new egocentric observation (facing nor...

✅ Evaluation Results

id: 079f24d0e9023ee5

question: You move to a new location and face north. You observe: pillow is at front, mid distance; books is at front-right, mid distance 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: [2, 1]

final_pos: [9, 10]

final_ori: [0, 1]

room_id: 3

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

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

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.409

raw_score: 0.409

score: 0.409

evaluation_mode: prompt_cogmap

Question 3 (ID: db6d367fcaa0e174)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: television is at front-right, slightly far, facing left; suitcase is at front-left, mid distance, facing backward; chair is at front, mid distance, facing left
Treat the yellow door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, ...

🤔 Assistant Answer (click to toggle)
THINK:
I will provide one consistent integer-coordinate global map (origin at the agent’s initial start, +y = north) that fits the observed qualitative bearings/distances and door jumps. Then, using that map, convert to the coordinate frame where the yellow door is (0,0) and solve for the agent posi...

✅ Evaluation Results

id: db6d367fcaa0e174

question: You move to a new location and face east. You observe: television is at front-right, slightly far, facing left; suitcase is at front-left, mid distance, facing backward; chair is at front, mid distance, facing left Treat the yellow 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: [-2, -4]

final_pos: [3, 3]

final_ori: [1, 0]

room_id: 2

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

final_observation: [{'name': 'television', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'suitcase', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'chair', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: prompt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: c9de6790a897c673)

❓ 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 desklamp.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from desklamp (-3,0) to truck (-5,2): (-2,+2) → north-west; distance √8≈2.83 → mid.
FINAL ANSWER:
north-west, mid distance...

✅ Evaluation Results

id: c9de6790a897c673

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

action: None

answer: north west, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 7360e44dad33a0d5)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from chair (1,-8) to books (5,2): (+4, +10) which is mostly north; distance sqrt(4^2+10^2)≈10.77 → far.
FINAL ANSWER:
north, far...

✅ Evaluation Results

id: 7360e44dad33a0d5

question: You return to your starting position and face north. From a Top-Down map, describe where books is relative to 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: 50faba75f48ab8cb)

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

🤔 Assistant Answer (click to toggle)
THINK:
Backpack at (3,-2) is 6 units east and 4 units north of table at (-3,-6), which is north-east and about 7.2 units away (slightly far).
FINAL ANSWER:
north-east, slightly far...

✅ Evaluation Results

id: 50faba75f48ab8cb

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

action: None

answer: north east, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: fffaede931ebf4f3)

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

🤔 Assistant Answer (click to toggle)
THINK:
At laptop (-1,2) facing south; desklamp at (-3,0) is 2 forward and 2 right in my frame (45°), distance √8 ≈ 2.83 → mid distance.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: fffaede931ebf4f3

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

Question 2 (ID: b8cd5a53479d1c3e)

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

🤔 Assistant Answer (click to toggle)
THINK:
After jumping to backpack, you are at (3, -2) facing north. Books at (5, 2) is 2 right and 4 forward; angle ≈ 26.6° (front-right) and distance √20 ≈ 4.47 (slightly far).
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: b8cd5a53479d1c3e

question: Now you jump to backpack's direction, facing its direction. Describe where books 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: a67a1cef66054f66)

❓ Evaluation Question (click to toggle)
Now you jump to desklamp'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 desklamp (-3,0) facing east, laptop (-1,2) is 2 forward and 2 left; distance √8≈2.83 → mid distance.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: a67a1cef66054f66

question: Now you jump to desklamp'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-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 5902e70da1b23e88)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that laptop is front-left, mid distance, facing right; red 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:
Test which object’s position/facing makes laptop appear front-left at mid distance and with laptop’s absolute facing (south) show up as “facing right”. Standing at the desklamp (-3,0) facing east: laptop is at vector (2,2) → front-left, distance √8 ≈ 2.8 (mid), and laptop facing south is to t...

✅ Evaluation Results

id: 5902e70da1b23e88

question: Now you jump to an object's position, facing its direction. You observe that laptop is front-left, mid distance, facing right; red 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: desklamp

final_pos: [3, 11]

final_ori: [1, 0]

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

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 7f5c758512898b6b)

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

🤔 Assistant Answer (click to toggle)
THINK:
Shoppingcart is facing north but appears “facing backward,” implying I’m facing south. Desklamp faces east but appears “facing left,” also consistent with facing south (left = east). The only object I could have jumped to that faces south is the laptop, and from the laptop’s position the bear...

✅ Evaluation Results

id: 7f5c758512898b6b

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

action: None

answer:

answer: laptop

final_pos: [5, 13]

final_ori: [0, -1]

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

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 7f4f9f9c8d6112b9)

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

🤔 Assistant Answer (click to toggle)
THINK:
Test candidate positions/orientations against the observed egocentric bearings/distances to bike, pillow, and books. Standing at backpack (3,-2) facing north gives pillow at (0,+4) → front, mid; bike at (+2,+2) → front-right, mid; books at (+2,+4) → front-right, slightly far. This matches exa...

✅ Evaluation Results

id: 7f4f9f9c8d6112b9

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

action: None

answer:

answer: backpack

final_pos: [9, 9]

final_ori: [0, 1]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'pillow', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'books', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 3

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 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: 286b5d2b65475d4f)

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

After executing the actions, what is the ego relation o...

🤔 Assistant Answer (click to toggle)
THINK:
After the moves, I end at the yellow door at (-1, -4) facing north. The laptop is at (-1, 2), which is directly in front by 6 units, placing it in the slightly-far distance bin.
FINAL ANSWER:
front, slightly far...

✅ Evaluation Results

id: 286b5d2b65475d4f

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front, mid distance. 3. Rotate(-90) 4. Jump to the object at front-left, slightly far. 5. 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, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: e413d4447d27b08e)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Track final pose after the rotations and jumps, then compute suitcase’s bearing and distance from the final agent position (facing north) using the ego angle/distance bins.
FINAL ANSWER:
front-left, slightly far...

✅ Evaluation Results

id: e413d4447d27b08e

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

action: None

answer: front-left, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_gt_cogmap

Question 3 (ID: 45597bafd7e0cdbc)

❓ 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-left, mid distance.
3. Rotate(90)
4. Jump to the object at front-right, slightly far.
5. Rotate(-180)

After executing the actions, what is the ego relat...

🤔 Assistant Answer (click to toggle)
THINK:
Compute final agent pose after the given rotates/jumps, then compute desklamp’s bearing/distance from that pose and bin them into the required egocentric direction and distance categories.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 45597bafd7e0cdbc

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-left, mid distance. 3. Rotate(90) 4. Jump to the object at front-right, slightly far. 5. Rotate(-180) After executing the actions, what is the ego relation of desklamp relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 5ed57112b3bfdc2e)

❓ 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:
laptop is at front-left, slightly far, facing backward; pillow is at front-right, slightly far; books is at front-right, sligh...

🤔 Assistant Answer (click to toggle)
THINK:
We need end at (1,-2) facing north (this yields laptop front-left slightly far; pillow/books front-right slightly far). From start (0,0) facing north, rotate to face south to bring red door into FOV, jump to it, then rotate back to north.
FINAL ANSWER:
Rotate(180), JumpTo(red door), Rotate(18...

✅ Evaluation Results

id: 5ed57112b3bfdc2e

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: laptop is at front-left, slightly far, facing backward; pillow is at front-right, slightly far; books is at front-right, slightly far 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, 9]

final_ori: [0, 1]

room_id: [1, 3]

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

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

final_observation: [{'name': 'laptop', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'pillow', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'books', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [7.0, 9.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 1f898b79fbf64cc4)

❓ 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:
shoppingcart is at front-left, mid distance, facing forward; laptop is at front, slightly far, facing backward; desklamp is at...

🤔 Assistant Answer (click to toggle)
THINK:
Final view matches being at the yellow door coordinate (-1, -4) facing north. From start (0,0) facing north: rotate to face south so the yellow door is in FOV, jump to it, then rotate back to north.
FINAL ANSWER:
Rotate(180), JumpTo(yellow door), Rotate(180)...

✅ Evaluation Results

id: 1f898b79fbf64cc4

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: shoppingcart is at front-left, mid distance, facing forward; laptop is at front, slightly far, facing backward; desklamp is at front-left, slightly far, 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: [5, 7]

final_ori: [0, 1]

room_id: [2, 1]

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

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

final_observation: [{'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'laptop', 'direction': 'front', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'desklamp', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [5.0, 7.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 8a8f0522e5b332f6)

❓ 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:
backpack is at front, mid distance, facing backward; bike is at front-left, mid distance, facing backward; red door is at fron...

🤔 Assistant Answer (click to toggle)
THINK:
Find a pose where backpack is straight ahead, bike is front-left, and red door is front-right. This matches being at pillow (3,2) facing south. From start (0,0) facing north: rotate east to bring pillow into FOV, jump to pillow, then rotate south.
FINAL ANSWER:
Rotate(90), JumpTo(pillow), Rot...

✅ Evaluation Results

id: 8a8f0522e5b332f6

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: backpack is at front, mid distance, facing backward; bike is at front-left, mid distance, facing backward; red 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: [9, 13]

final_ori: [0, -1]

room_id: 3

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'red door'], ['rotate', 180], ['jumpto', 'pillow'], ['rotate', 180]]

final_observation: [{'name': 'backpack', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'bike', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'red 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: [9.0, 13.0]

final_ori: [0, -1]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 2fec3ac651cf4a7c)

❓ 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, desklamp, chair, 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 ground-truth map coordinates for the requested objects in the specified order.
FINAL ANSWER:
(-5, 2); (-3, 0); (1, -8); (-1, 2)...

✅ Evaluation Results

id: 2fec3ac651cf4a7c

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

action: None

answer: [[-5, 2], [-3, 0], [1, -8], [-1, 2]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 237ae2871d72362b)

❓ 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: backpack, chair, bike.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided ground-truth cognitive map coordinates for the requested objects in the given order.
FINAL ANSWER:
(3, -2); (1, -8); (5, 0)...

✅ Evaluation Results

id: 237ae2871d72362b

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

action: None

answer: [[3, -2], [1, -8], [5, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 0ca18bd6fca57b5f)

❓ 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: table, backpack, bike, books.
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 the specified order.
FINAL ANSWER:
(-3, -6); (3, -2); (5, 0); (5, 2)...

✅ Evaluation Results

id: 0ca18bd6fca57b5f

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

action: None

answer: [[-3, -6], [3, -2], [5, 0], [5, 2]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 4831d162b78edf8c)

❓ 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: chair, desklamp, laptop, table.
List...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s bearing from the origin with initial facing north, then order by when the agent’s forward direction matches those bearings during a continuous counterclockwise rotation (decreasing angle, wrapping past -180°). Bearings: laptop ≈ -26.6°, desklamp = -90°, table ≈ -153.4°, ...

✅ Evaluation Results

id: 4831d162b78edf8c

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: chair, desklamp, laptop, table. 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: ['laptop', 'desklamp', 'table', 'chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: b3bb94ba48268066)

❓ 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 clockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: backpack, bike, chair, pillow, shoppingcart...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s clockwise bearing from north using its (x,y) relative to the agent, then sort by increasing bearing from 0° to 360°.
FINAL ANSWER:
pillow, bike, backpack, chair, shoppingcart...

✅ Evaluation Results

id: b3bb94ba48268066

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning clockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: backpack, bike, chair, pillow, 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: ['pillow', 'bike', 'backpack', 'chair', 'shoppingcart']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: a73f3abfbc74d987)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s bearing from the agent (facing north), then list them in the order encountered when rotating counterclockwise from 0° through -180° to +180° back to 0°: laptop (-26.6°), truck (-68.2°), table (-153.4°), television (+174.3°).
FINAL ANSWER:
laptop, truck, table, television...

✅ Evaluation Results

id: a73f3abfbc74d987

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, table, television, 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: ['laptop', 'truck', 'table', 'television']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: c98da85995bf0909)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
From (0,1) facing west, truck at (-5,2) is mostly forward with a small rightward offset; distance ≈ 5.1.
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: c98da85995bf0909

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

action: None

answer: front-slight-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 233021678b15a057)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (2, 1) and face north.
What is the egocentric relation of pillow?

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

🤔 Assistant Answer (click to toggle)
THINK:
With red door as origin, pillow is at (2,4). From agent at (2,1) facing north, pillow is at (0,3) straight ahead; distance 3 is mid distance.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: 233021678b15a057

question: Treat the red door as the new 'origin' (0, 0). You move to (2, 1) and face north. What is the egocentric relation of pillow? 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

Question 3 (ID: 5807e77e81650360)

❓ Evaluation Question (click to toggle)
Treat the yellow door as the new 'origin' (0, 0).
You move to (-2, -4) and face east.
What is the egocentric relation of television?

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

🤔 Assistant Answer (click to toggle)
THINK:
With yellow door as (0,0), television is at (2,-6) and agent at (-2,-4) facing east. Relative vector to television is (4,-2): forward 4, right 2 ⇒ front-right. Distance √20 ≈ 4.47 ⇒ slightly far.
FINAL ANSWER:
front-right, slightly far...

✅ Evaluation Results

id: 5807e77e81650360

question: Treat the yellow door as the new 'origin' (0, 0). You move to (-2, -4) and face east. What is the egocentric relation of television? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 3ba4cbab06925eb6)

❓ Evaluation Question (click to toggle)
You move to a new location and face west.
You observe: truck is at front-slight-right, slightly far, facing forward; shoppingcart is at front-left, slightly far, facing right; desklamp is at front-slight-left, mid distance, facing backward
Still treat your initial position as origin (0, 0)
What is y...

🤔 Assistant Answer (click to toggle)
THINK:
Use known object coordinates (truck -5,2; shoppingcart -3,-2; desklamp -3,0). Facing west means “front” is toward decreasing x. Find integer agent position where desklamp is west-slight-south at 2–4m, shoppingcart is west-south at 4–8m, and truck is west-slight-north at 4–8m; (0,1) satisfies ...

✅ Evaluation Results

id: 3ba4cbab06925eb6

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

action: None

answer:

coord: [0, 1]

final_pos: [6, 12]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

final_observation: [{'name': 'truck', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'desklamp', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing backward'}]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 079f24d0e9023ee5)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: pillow is at front, mid distance; books is at front-right, mid distance
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 agent facing north, “pillow at front” implies the agent shares pillow’s x-coordinate and is south of it. Choosing the integer position that makes both pillow and books at “mid distance” and books “front-right” gives agent at (3, -1) in the original frame. Re-expressing with red door at (...

✅ Evaluation Results

id: 079f24d0e9023ee5

question: You move to a new location and face north. You observe: pillow is at front, mid distance; books is at front-right, mid distance 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: [2, 1]

final_pos: [9, 10]

final_ori: [0, 1]

room_id: 3

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

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

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: db6d367fcaa0e174)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: television is at front-right, slightly far, facing left; suitcase is at front-left, mid distance, facing backward; chair is at front, mid distance, facing left
Treat the yellow door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, ...

🤔 Assistant Answer (click to toggle)
THINK:
Infer current agent position using known object coordinates and the egocentric bearings while facing east: chair “front” implies same y as agent and greater x; matching all three observations gives agent at (-3, -8) in the original frame. Re-express relative to yellow door at (-1, -4): (-3+1,...

✅ Evaluation Results

id: db6d367fcaa0e174

question: You move to a new location and face east. You observe: television is at front-right, slightly far, facing left; suitcase is at front-left, mid distance, facing backward; chair is at front, mid distance, facing left Treat the yellow 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: [-2, -4]

final_pos: [3, 3]

final_ori: [1, 0]

room_id: 2

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

final_observation: [{'name': 'television', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'suitcase', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'chair', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: c9de6790a897c673)

❓ 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 desklamp.
Answer format: <cardinal direction>, <distance>
Example: north-west, near
...

🤔 Assistant Answer (click to toggle)
THINK: Truck at (-3,0) is 1 left and 1 up from desklamp at (-2,-1), so it is to the north-west and at near distance (~1.41).
FINAL ANSWER:
north-west, near...

✅ Evaluation Results

id: c9de6790a897c673

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

action: None

answer: north west, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 2 (ID: 7360e44dad33a0d5)

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

🤔 Assistant Answer (click to toggle)
THINK: Using the global coordinates: chair at (3,-9), books at (5,2). From chair to books is Δx=+2 (east), Δy=+11 (north) → bearing ≈ 10° (north bin). Distance ≈ 11.2 → far.
FINAL ANSWER:
north, far...

✅ Evaluation Results

id: 7360e44dad33a0d5

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

Question 3 (ID: 50faba75f48ab8cb)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from table (-2,-7) to backpack (3,-3): dx=+5 (east), dy=+4 (north) → north-east; distance √(5²+4²)=√41≈6.4 → slightly far.
FINAL ANSWER:
north-east, slightly far...

✅ Evaluation Results

id: 50faba75f48ab8cb

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

action: None

answer: north east, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: fffaede931ebf4f3)

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

🤔 Assistant Answer (click to toggle)
THINK:
At the laptop position facing south, the desklamp lies mostly ahead (south) and a bit to the right (west); distance is just over 4 units.
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: fffaede931ebf4f3

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

evaluation_mode: use_model_cogmap

Question 2 (ID: b8cd5a53479d1c3e)

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

🤔 Assistant Answer (click to toggle)
THINK:
From backpack at (3,-3) facing north, books at (5,2) is 2 right and 5 forward: about +22° bearing (front-slight-right) and ~5.4 units away (slightly far).
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: b8cd5a53479d1c3e

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

action: None

answer: front-right, slightly far

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 3 (ID: a67a1cef66054f66)

❓ Evaluation Question (click to toggle)
Now you jump to desklamp'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:
After jumping, I’m at the desklamp position facing east; the laptop is at vector (dx,dy)=(+1,+4) in global, which is ~-76° (to my left, outside ±45°), at distance √17≈4.12 → slightly far.
FINAL ANSWER:
beyond-fov, slightly far...

✅ Evaluation Results

id: a67a1cef66054f66

question: Now you jump to desklamp'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-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 5902e70da1b23e88)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that laptop is front-left, mid distance, facing right; red 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:
Laptop’s absolute facing is south (it was “facing backward” when I faced north). If laptop now appears “facing right,” then my right side must be south, meaning I’m facing east. The only observed object with absolute facing east is the desklamp.
FINAL ANSWER:
desklamp...

✅ Evaluation Results

id: 5902e70da1b23e88

question: Now you jump to an object's position, facing its direction. You observe that laptop is front-left, mid distance, facing right; red 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: desklamp

final_pos: [3, 11]

final_ori: [1, 0]

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

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 7f5c758512898b6b)

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

🤔 Assistant Answer (click to toggle)
THINK: Shoppingcart previously appeared facing “right” when my view-axis made “right” align with north, so shoppingcart faces north. In this query it faces “backward”, implying I’m facing south. The object I jumped to must face south; the laptop was observed facing backward from the origin (therefor...

✅ Evaluation Results

id: 7f5c758512898b6b

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

action: None

answer:

answer: laptop

final_pos: [5, 13]

final_ori: [0, -1]

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

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 1

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 7f4f9f9c8d6112b9)

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

🤔 Assistant Answer (click to toggle)
THINK: Bike’s observed facing is “forward”, so I must be facing the same global direction as the bike (north). The view (bike front-right; pillow front; books front-right farther) best matches standing at the nearby north-facing object that was previously observed close to the bike/books cluster: th...

✅ Evaluation Results

id: 7f4f9f9c8d6112b9

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

action: None

answer:

answer: backpack

final_pos: [9, 9]

final_ori: [0, 1]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'pillow', 'direction': 'front', 'distance': 'mid distance', 'orientation': None}, {'name': 'books', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

object_positions:

laptop: [5.0, 13.0]

television: [7.0, 1.0]

backpack: [9.0, 9.0]

desklamp: [3.0, 11.0]

suitcase: [5.0, 5.0]

bike: [11.0, 11.0]

shoppingcart: [3.0, 9.0]

truck: [1.0, 13.0]

chair: [7.0, 3.0]

pillow: [9.0, 13.0]

table: [3.0, 5.0]

books: [11.0, 13.0]

red door: [7.0, 9.0]

yellow door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

room_id: 3

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 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: 286b5d2b65475d4f)

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

After executing the actions, what is the ego relation o...

🤔 Assistant Answer (click to toggle)
THINK:
After the moves, I’m at the yellow door and end up facing north. The laptop is directly ahead (same x), 6 units away, which falls in the slightly-far bin.
FINAL ANSWER:
front, slightly far...

✅ Evaluation Results

id: 286b5d2b65475d4f

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front, mid distance. 3. Rotate(-90) 4. Jump to the object at front-left, slightly far. 5. 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, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: e413d4447d27b08e)

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

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

🤔 Assistant Answer (click to toggle)
THINK: From start facing south, the front-slight-right slightly-far target is the yellow door; from the yellow door facing south, the front-left slightly-far target is the chair. After rotating to face north at the chair, the suitcase lies outside the ±45° front FOV and at a slightly-far distance.
F...

✅ Evaluation Results

id: e413d4447d27b08e

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

action: None

answer: front-left, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: 45597bafd7e0cdbc)

❓ 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-left, mid distance.
3. Rotate(90)
4. Jump to the object at front-right, slightly far.
5. Rotate(-180)

After executing the actions, what is the ego relat...

🤔 Assistant Answer (click to toggle)
THINK: From start face north, rotate to face west; the front-left mid object is desklamp. Rotate to north; jump to the front-right slightly-far object (laptop). Rotate to face south. From laptop, desklamp lies mostly ahead and slightly to the right at ~4.1 units.
FINAL ANSWER:
front-slight-right, sl...

✅ Evaluation Results

id: 45597bafd7e0cdbc

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-left, mid distance. 3. Rotate(90) 4. Jump to the object at front-right, slightly far. 5. Rotate(-180) After executing the actions, what is the ego relation of desklamp relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 5ed57112b3bfdc2e)

❓ 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:
laptop is at front-left, slightly far, facing backward; pillow is at front-right, slightly far; books is at front-right, sligh...

🤔 Assistant Answer (click to toggle)
THINK: The final view matches being at the red door facing north; from the start (facing north), rotate to face south to bring the red door into FOV, jump to it, then rotate to face north.
FINAL ANSWER:
Rotate(180), JumpTo(red door), Rotate(180)...

✅ Evaluation Results

id: 5ed57112b3bfdc2e

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: laptop is at front-left, slightly far, facing backward; pillow is at front-right, slightly far; books is at front-right, slightly far 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, 9]

final_ori: [0, 1]

room_id: [1, 3]

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

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

final_observation: [{'name': 'laptop', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'pillow', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}, {'name': 'books', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [7.0, 9.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 1f898b79fbf64cc4)

❓ 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:
shoppingcart is at front-left, mid distance, facing forward; laptop is at front, slightly far, facing backward; desklamp is at...

🤔 Assistant Answer (click to toggle)
THINK: To get that view, the agent must end up at the yellow door facing north (where laptop is front and shoppingcart/desklamp are front-left). From the start (facing north), rotate to face south so the yellow door is in FOV, jump to it, then rotate 180° to face north.
FINAL ANSWER:
Rotate(180), Ju...

✅ Evaluation Results

id: 1f898b79fbf64cc4

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: shoppingcart is at front-left, mid distance, facing forward; laptop is at front, slightly far, facing backward; desklamp is at front-left, slightly far, 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: [5, 7]

final_ori: [0, 1]

room_id: [2, 1]

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

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

final_observation: [{'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'laptop', 'direction': 'front', 'distance': 'slightly far', 'orientation': 'facing backward'}, {'name': 'desklamp', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [5.0, 7.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 8a8f0522e5b332f6)

❓ 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:
backpack is at front, mid distance, facing backward; bike is at front-left, mid distance, facing backward; red door is at fron...

🤔 Assistant Answer (click to toggle)
THINK: To see the red door from the start, first rotate to face south, jump to the red door, rotate to face north to bring the pillow into view for jumping, then rotate back to face south to match the final observed bearings.
FINAL ANSWER:
Rotate(180), JumpTo(red door), Rotate(180), JumpTo(pillow), ...

✅ Evaluation Results

id: 8a8f0522e5b332f6

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: backpack is at front, mid distance, facing backward; bike is at front-left, mid distance, facing backward; red 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: [9, 13]

final_ori: [0, -1]

room_id: 3

init_pos: [6, 11]

init_ori: [0, 1]

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'red door'], ['rotate', 180], ['jumpto', 'pillow'], ['rotate', 180]]

final_observation: [{'name': 'backpack', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'bike', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'red 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: [9.0, 13.0]

final_ori: [0, -1]

visible_match: True

score: 1

evaluation_mode: use_model_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 2fec3ac651cf4a7c)

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

🤔 Assistant Answer (click to toggle)
THINK:
Read off the requested objects’ allocentric coordinates (origin at initial agent position) from the completed global map.
FINAL ANSWER:
(-3, 0); (-2, -1); (3, -9); (-1, 3)...

✅ Evaluation Results

id: 2fec3ac651cf4a7c

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

action: None

answer: [[-5, 2], [-3, 0], [1, -8], [-1, 2]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.640

threshold: 0.900

score: 0.640

evaluation_mode: use_model_cogmap

Question 2 (ID: 237ae2871d72362b)

❓ 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: backpack, chair, bike.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK: Use the previously constructed global cognitive map with the initial agent position as origin; read off allocentric coordinates for backpack, chair, bike in that order.
FINAL ANSWER:
(3, -3); (3, -9); (5, 1)...

✅ Evaluation Results

id: 237ae2871d72362b

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

action: None

answer: [[3, -2], [1, -8], [5, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.802

threshold: 0.900

score: 0.802

evaluation_mode: use_model_cogmap

Question 3 (ID: 0ca18bd6fca57b5f)

❓ 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: table, backpack, bike, books.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Use the previously constructed global cognitive map coordinates referenced to the initial origin (0,0), and report the requested objects in order.
FINAL ANSWER:
(-2, -7); (3, -3); (5, 1); (5, 2)...

✅ Evaluation Results

id: 0ca18bd6fca57b5f

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

action: None

answer: [[-3, -6], [3, -2], [5, 0], [5, 2]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.814

threshold: 0.900

score: 0.814

evaluation_mode: use_model_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 4831d162b78edf8c)

❓ 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: chair, desklamp, laptop, table.
List...

🤔 Assistant Answer (click to toggle)
THINK: Compute each object’s bearing from origin with 0° at north (bearing = atan2(dx,dy)); then order by when a counterclockwise rotation from 0° to -360° aligns with each bearing (using equivalent negative angles past -180°). Bearings: laptop ≈ -18.4°, desklamp ≈ -116.6°, table ≈ -164.1°, chair ≈ ...

✅ Evaluation Results

id: 4831d162b78edf8c

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: chair, desklamp, laptop, table. 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: ['laptop', 'desklamp', 'table', 'chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: b3bb94ba48268066)

❓ 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 clockwise in place.
Assume all walls are removed (you can see through walls), so every object is visible.
Focus on this set of objects: backpack, bike, chair, pillow, shoppingcart...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s clockwise bearing from the origin (0,0) with 0° at north using atan2(dx, dy), convert to [0,360) and sort ascending: pillow (~63°), bike (~79°), backpack (135°), chair (~162°), shoppingcart (~214°).
FINAL ANSWER:
pillow, bike, backpack, chair, shoppingcart...

✅ Evaluation Results

id: b3bb94ba48268066

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning clockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: backpack, bike, chair, pillow, 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: ['pillow', 'bike', 'backpack', 'chair', 'shoppingcart']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: a73f3abfbc74d987)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s bearing from the origin (facing north) and order them by when that bearing aligns with the forward direction during a continuous counterclockwise rotation.
FINAL ANSWER:
laptop, truck, table, television...

✅ Evaluation Results

id: a73f3abfbc74d987

question: You return to your starting position and face north. You will perform a full 360-degree rotation by continuously turning counterclockwise in place. Assume all walls are removed (you can see through walls), so every object is visible. Focus on this set of objects: laptop, table, television, 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: ['laptop', 'truck', 'table', 'television']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: c98da85995bf0909)

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

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

🤔 Assistant Answer (click to toggle)
THINK: From (0,1) facing west, truck at (-3,0) is 3 units forward and 1 unit left (bearing ≈ -18.4°), at distance √10 ≈ 3.16 → mid distance.
FINAL ANSWER:
front-slight-left, mid distance...

✅ Evaluation Results

id: c98da85995bf0909

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

action: None

answer: front-slight-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: 233021678b15a057)

❓ Evaluation Question (click to toggle)
Treat the red door as the new 'origin' (0, 0).
You move to (2, 1) and face north.
What is the egocentric relation of pillow?

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

🤔 Assistant Answer (click to toggle)
THINK:
With red door as origin, pillow is at (2,5). From your position (2,1) facing north, the offset to pillow is (0,4), i.e., straight ahead at distance 4 → mid distance.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: 233021678b15a057

question: Treat the red door as the new 'origin' (0, 0). You move to (2, 1) and face north. What is the egocentric relation of pillow? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 5807e77e81650360)

❓ Evaluation Question (click to toggle)
Treat the yellow door as the new 'origin' (0, 0).
You move to (-2, -4) and face east.
What is the egocentric relation of television?

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

🤔 Assistant Answer (click to toggle)
THINK: Convert television to yellow-door-centered coords, compute vector from agent at (-2,-4) facing east, then bin by egocentric angle and distance.
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: 5807e77e81650360

question: Treat the yellow door as the new 'origin' (0, 0). You move to (-2, -4) and face east. What is the egocentric relation of television? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 3ba4cbab06925eb6)

❓ Evaluation Question (click to toggle)
You move to a new location and face west.
You observe: truck is at front-slight-right, slightly far, facing forward; shoppingcart is at front-left, slightly far, facing right; desklamp is at front-slight-left, mid distance, facing backward
Still treat your initial position as origin (0, 0)
What is y...

🤔 Assistant Answer (click to toggle)
THINK: Using the previously mapped coordinates of truck, shoppingcart, and desklamp, solve for the integer agent location (facing west) whose distances match the bins (truck & shoppingcart slightly far; desklamp mid) and whose relative bearings match (truck slightly right of front; shoppingcart left...

✅ Evaluation Results

id: 3ba4cbab06925eb6

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

action: None

answer:

coord: [0, 1]

final_pos: [6, 12]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

final_observation: [{'name': 'truck', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'shoppingcart', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'desklamp', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing backward'}]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.059

raw_score: 0.059

score: 0.059

evaluation_mode: use_model_cogmap

Question 2 (ID: 079f24d0e9023ee5)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: pillow is at front, mid distance; books is at front-right, mid distance
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 red door as (0,0), pillow is at (2,5) and books at (3,5). Facing north, to see pillow straight ahead at mid distance and books at front-right mid distance, the agent must be at (2,3), giving vectors to pillow (0,2) and to books (1,2).
FINAL ANSWER:
(2, 3)...

✅ Evaluation Results

id: 079f24d0e9023ee5

question: You move to a new location and face north. You observe: pillow is at front, mid distance; books is at front-right, mid distance 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: [2, 1]

final_pos: [9, 10]

final_ori: [0, 1]

room_id: 3

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

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

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.409

raw_score: 0.409

score: 0.409

evaluation_mode: use_model_cogmap

Question 3 (ID: db6d367fcaa0e174)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: television is at front-right, slightly far, facing left; suitcase is at front-left, mid distance, facing backward; chair is at front, mid distance, facing left
Treat the yellow door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, ...

🤔 Assistant Answer (click to toggle)
THINK: Use the earlier yellow-door-centered placement consistent with both observation sets: suitcase at (0,-2) (front near from the yellow door when facing south), and choose the new agent position so chair is exactly in front (east) at mid distance while suitcase is front-left at mid distance; the...

✅ Evaluation Results

id: db6d367fcaa0e174

question: You move to a new location and face east. You observe: television is at front-right, slightly far, facing left; suitcase is at front-left, mid distance, facing backward; chair is at front, mid distance, facing left Treat the yellow 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: [-2, -4]

final_pos: [3, 3]

final_ori: [1, 0]

room_id: 2

object_positions:

laptop: [5, 13]

television: [7, 1]

backpack: [9, 9]

desklamp: [3, 11]

suitcase: [5, 5]

bike: [11, 11]

shoppingcart: [3, 9]

truck: [1, 13]

chair: [7, 3]

pillow: [9, 13]

table: [3, 5]

books: [11, 13]

red door: [7, 9]

yellow door: [5, 7]

object_orientations:

laptop: [0, -1]

television: [0, 1]

backpack: [0, 1]

desklamp: [1, 0]

suitcase: [-1, 0]

bike: [0, 1]

shoppingcart: [0, 1]

truck: [-1, 0]

chair: [0, 1]

red door: [1, 0]

yellow door: [0, 1]

final_observation: [{'name': 'television', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'suitcase', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'chair', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

red door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

yellow door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

laptop: 1

television: 2

backpack: 3

desklamp: 1

suitcase: 2

bike: 3

shoppingcart: 1

truck: 1

chair: 2

pillow: 3

table: 2

books: 3

red door: [1, 3]

yellow door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_model_cogmap

Model: gpt-5.2

sample_run08

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

🧭 False Belief Exploration

🔄 FB Turn 14

🔄 FB Turn 15

🔄 FB Turn 16

🔄 FB Turn 17

🔄 FB Turn 18

🔄 FB Turn 19

🔄 FB Turn 20

🔄 FB Turn 21

🔄 FB Turn 22

📊 Evaluation (default)

📊 Task: DirectionEvaluationTask

Question 1 (ID: c9de6790a897c673)

Question 2 (ID: 7360e44dad33a0d5)

Question 3 (ID: 50faba75f48ab8cb)

📊 Task: PovEvaluationTask

Question 1 (ID: fffaede931ebf4f3)

Question 2 (ID: b8cd5a53479d1c3e)

Question 3 (ID: a67a1cef66054f66)

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 5902e70da1b23e88)

Question 2 (ID: 7f5c758512898b6b)

Question 3 (ID: 7f4f9f9c8d6112b9)

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 286b5d2b65475d4f)

Question 2 (ID: e413d4447d27b08e)

Question 3 (ID: 45597bafd7e0cdbc)

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 5ed57112b3bfdc2e)

Question 2 (ID: 1f898b79fbf64cc4)

Question 3 (ID: 8a8f0522e5b332f6)

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 2fec3ac651cf4a7c)

Question 2 (ID: 237ae2871d72362b)

Question 3 (ID: 0ca18bd6fca57b5f)

📊 Task: RotEvaluationTask

Question 1 (ID: 4831d162b78edf8c)

Question 2 (ID: b3bb94ba48268066)

Question 3 (ID: a73f3abfbc74d987)

📊 Task: RotDualEvaluationTask

Question 1 (ID: ac2a6bf257762996)

Question 2 (ID: fabe192c1f42a07a)

Question 3 (ID: 1fe9e0ec40250bfb)