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 (room 1) facing north to identify visible objects and any doors, so we can plan the most informative next moves....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assign integer coordinates consistent with the observed egocentric bearings and distance bins from the origin while facing north.
FINAL ANSWER: {
"agent": {
"position": [0, 0],
"facing": "north"
},
"green door": {
"position": [1, 3]
},
"blue door": {
"position": [-3,...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ]
  },
  "blue door": {
    "position": [
      -3,
      5
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Convert the two observed doors into a local (+x right, +y forward) integer-coordinate map consistent with their binned bearings/distances and noted wall positions.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"green door": {
"position": [1, 3]
},
"blue door": ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green door": {
      "position": [
        1,
        3
      ]
    },
    "blue door": {
      "position": [
        -3,
        6
      ]
    }
  }
}

🎯 Ground Truth

{
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green door": {
      "position": [
        1,
        3
      ]
    },
    "blue door": {
      "position": [
        -3,
        6
      ]
    }
  }
}

🎯 Ground Truth

{
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: From the agent’s current pose facing north with a 90° FOV (±45°), C/D/F fall within the forward sector, while A and B are too far to the left and E is directly left, all outside FOV and thus in unobserved regions.
FINAL ANSWER: {"unexplored":["A","B","E"]}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, E

🎯 Ground Truth: B, A, E

🧠 Cognitive Map Metrics

Global:

dir: 1

facing: 1

pos: 1

overall: 1

Global (Full):

dir: 0.050

facing: 0.091

pos: 0.200

overall: 0.114

Local:

dir: 1

facing: 1

pos: 0.912

overall: 0.971

Local (Newly):

dir: 1

facing: 1

pos: 0.912

overall: 0.971

Fog Probe:

precision: 1

recall: 1

overall: 1

📈 Turn Metrics

node_coverage: 0.143

edge_coverage: 0.049

is_action_fail: False

step: 1

action_counts:

move: 0

rotate: 0

return: 0

observe: 1

term: 0

forced_term: 0

query: 0

information_gain: 0.113

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 2

👤 Environment Observation (click to toggle)
You observe:
• green door: front-slight-right, mid distance, on right wall
• blue door: front-left, slightly far, on front wall.
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)
Jump to the nearer green door to stand in the doorway; from there the door becomes open/invisible and I should be able to observe into both connected rooms, maximizing new information per Observe()....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must include only objects observed so far (agent, green door, blue door) in a single global frame with integer coordinates; I’ll anchor the agent at the green door location since I jumped there, keeping initial origin at [0,0] facing north.
FINAL ANSWER:
{
"agent": { "position": [1, 3], "...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      1,
      3
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ]
  },
  "blue door": {
    "position": [
      -3,
      4
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      3
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      3
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: No objects (including doors) are currently in the field of view, so the local cognitive map contains an empty objects dictionary referenced to the agent origin.
FINAL ANSWER:
{
"origin": "agent",
"objects": {}
}...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The agent has only observed doors and no objects; all candidate-labeled locations remain in unobserved regions (no candidate point has been confirmed/covered by observation).
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L"]
}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

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

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

🎯 Ground Truth: L, D, C, F, I, A

🧠 Cognitive Map Metrics

Global:

dir: 0.667

facing: 1

pos: 0.927

overall: 0.865

Global (Full):

dir: 0.033

facing: 0.091

pos: 0.185

overall: 0.103

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.143

edge_coverage: 0.049

is_action_fail: False

step: 2

action_counts:

move: 1

rotate: 0

return: 0

observe: 2

term: 0

forced_term: 0

query: 0

information_gain: 0.113

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 3

👤 Environment Observation (click to toggle)
You jumped to green door. 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)
From the doorway my current facing (still north) likely points along the wall, so my 90° FOV misses both rooms’ interiors. Rotate 90° to face into the doorway (east/right) and observe to see into the connected room(s)....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assign integer coordinates consistent with binned distances/bearings from the initial pose and from the agent’s later doorway observation; convert object facing directions from the agent’s current global facing (east) into global cardinals.
FINAL ANSWER:
{
"agent": { "position": [1, 3], "f...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      1,
      3
    ]
  },
  "blue door": {
    "position": [
      -2,
      4
    ]
  },
  "table": {
    "position": [
      6,
      3
    ]
  },
  "bike": {
    "position": [
      6,
      4
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      4,
      4
    ],
    "facing": "west"
  },
  "truck": {
    "position": [
      4,
      2
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map using integer coordinates consistent with the observed egocentric bearings/distances and the given proximity relations (bike north of chair; bike west of table).
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"bike": {
"position": [-1, 4],
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "bike": {
      "position": [
        -1,
        4
      ],
      "facing": "+x"
    },
    "truck": {
      "position": [
        1,
        3
      ],
      "facing": "-x"
    },
    "chair": {
      "position": [
        -1,
        2
      ],
      "facing": "-y"
    },
    "table": {
      "position": [
        0,
        4
      ]
    }
  }
}

🎯 Ground Truth

{
  "bike": {
    "position": [
      -2,
      5
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      -2,
      3
    ],
    "facing": "west"
  },
  "truck": {
    "position": [
      2,
      3
    ],
    "facing": "north"
  },
  "table": {
    "position": [
      0,
      5
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "bike": {
      "position": [
        -1,
        4
      ],
      "facing": "+x"
    },
    "truck": {
      "position": [
        1,
        3
      ],
      "facing": "-x"
    },
    "chair": {
      "position": [
        -1,
        2
      ],
      "facing": "-y"
    },
    "table": {
      "position": [
        0,
        4
      ]
    }
  }
}

🎯 Ground Truth

{
  "bike": {
    "position": [
      -2,
      5
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      -2,
      3
    ],
    "facing": "west"
  },
  "truck": {
    "position": [
      2,
      3
    ],
    "facing": "north"
  },
  "table": {
    "position": [
      0,
      5
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: The agent has only observed into the right-side area from the green door; the left-side candidate cluster (A,C,D,F,H) lies in the unobserved region beyond the central divider and outside the observed FOV coverage.
FINAL ANSWER:
```json
{
"unexplored": ["A", "C", "D", "F", "H"]
}
```...

🗺️ Symbolic Fog Map

#################
#......##########
#......##########
#......##########
#......##########
#......##########
#......##########
#####+####......#
###......#.L....#
###.....K*......#
###H..C.F#......#
###...D..#BJ....#
###.A....#IG..E.#
###......########
#################

🔍 Fog Probe JSONs

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

🤖 Predicted: A, C, D, F, H

🎯 Ground Truth: A, D, H, G, I, B

🧠 Cognitive Map Metrics

Global:

dir: 0.714

facing: 1

pos: 0.896

overall: 0.870

Global (Full):

dir: 0.167

facing: 0.364

pos: 0.418

overall: 0.316

Local:

dir: 0.800

facing: 1

pos: 0.852

overall: 0.884

Local (Newly):

dir: 0.800

facing: 1

pos: 0.852

overall: 0.884

Fog Probe:

precision: 0.600

recall: 0.500

overall: 0.545

📈 Turn Metrics

node_coverage: 0.429

edge_coverage: 0.146

is_action_fail: False

step: 3

action_counts:

move: 1

rotate: 1

return: 0

observe: 3

term: 0

forced_term: 0

query: 0

information_gain: 0.384

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 4

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

🤔 Assistant Thinking (click to toggle)
We’ve only seen 4/12 objects, likely all in the room beyond the green door (room 2). To efficiently discover the remaining objects, use the doorway advantage and rotate 180° to look back into room 1; an Observe() from the doorway can reveal room 1 contents without moving....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a single consistent integer-coordinate global map (origin at initial agent pose), using current agent pose after the last Observe(); include only observed items and add facing only where reported (converted to global cardinal based on agent orientation at observation time).
FINAL ANSWER:...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      1,
      3
    ],
    "facing": "west"
  },
  "green door": {
    "position": [
      1,
      3
    ]
  },
  "blue door": {
    "position": [
      -4,
      6
    ]
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "table": {
    "position": [
      6,
      3
    ]
  },
  "laptop": {
    "position": [
      -4,
      0
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -4,
      2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      3
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      1,
      3
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) map using the current FOV only; assign integer coordinates consistent with the observed bearing/distance bins and convert object facing (left/right) into local axes (-x/+x).
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"laptop": {
"position": [-...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "laptop": {
      "position": [
        -3,
        5
      ],
      "facing": "-x"
    },
    "pan": {
      "position": [
        -2,
        3
      ],
      "facing": "-x"
    },
    "teddybear": {
      "position": [
        -1,
        5
      ],
      "facing": "+x"
    },
    "desklamp": {
      "position": [
        1,
        5
      ],
      "facing": "+x"
    },
    "blue door": {
      "position": [
        3,
        5
      ]
    }
  }
}

🎯 Ground Truth

{
  "pan": {
    "position": [
      -2,
      3
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      -4,
      5
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -2,
      6
    ],
    "facing": "north"
  },
  "blue door": {
    "position": [
      2,
      4
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      1,
      6
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "laptop": {
      "position": [
        -3,
        5
      ],
      "facing": "-x"
    },
    "pan": {
      "position": [
        -2,
        3
      ],
      "facing": "-x"
    },
    "teddybear": {
      "position": [
        -1,
        5
      ],
      "facing": "+x"
    },
    "desklamp": {
      "position": [
        1,
        5
      ],
      "facing": "+x"
    },
    "blue door": {
      "position": [
        3,
        5
      ]
    }
  }
}

🎯 Ground Truth

{
  "pan": {
    "position": [
      -2,
      3
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      -4,
      5
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -2,
      6
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      1,
      6
    ],
    "facing": "north"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: Candidate labels (A, B, C, D, E, F, G, H, I, J, K, L) all appear within the currently mapped accessible areas around the agent and not in any separate fogged/unexplored compartment beyond the visible doors in this probe.
FINAL ANSWER: {"unexplored":[]}...

🗺️ Symbolic Fog Map

#################
#......##########
#......##########
#......##########
#......##########
#......##########
#......##########
#####+####......#
###...D..#.....K#
###......*......#
###......#...B..#
###...F..#H.I...#
###L...E.#JC....#
###....GA########
#################

🔍 Fog Probe JSONs

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

🤖 Predicted: (none)

🎯 Ground Truth: A, E, G, H, C, J

🧠 Cognitive Map Metrics

Global:

dir: 0.909

facing: 1

pos: 0.916

overall: 0.942

Global (Full):

dir: 0.500

facing: 0.727

pos: 0.672

overall: 0.633

Local:

dir: 0.733

facing: 1

pos: 0.867

overall: 0.867

Local (Newly):

dir: 0.800

facing: 1

pos: 0.878

overall: 0.893

Fog Probe:

precision: 0

recall: 0

overall: 0

📈 Turn Metrics

node_coverage: 0.714

edge_coverage: 0.268

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: 0.587

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 5

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• laptop: front-left, slightly far, facing left
• pan: front-left, mid distance, facing left
• teddybear: front-slight-left, slightly far, facing right
• desklamp: front-slight-right, slightly far, facing right
• blue door: front-right, slightly far, on righ...

🤔 Assistant Thinking (click to toggle)
The blue door is visible from here, and likely leads to the third room containing the remaining unseen objects. Jump to the blue door to gain doorway visibility into its connected rooms, then observe to discover new objects....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a single global JSON map (origin at initial agent position). Use integer coordinates consistent with all observations from the two doorway viewpoints; include all observed objects/doors and facing for objects where provided. Agent is currently at the blue door, facing west.
FINAL ANSWER:...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -4,
      6
    ],
    "facing": "west"
  },
  "green door": {
    "position": [
      1,
      3
    ]
  },
  "blue door": {
    "position": [
      -4,
      6
    ]
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "table": {
    "position": [
      6,
      3
    ]
  },
  "laptop": {
    "position": [
      -3,
      -1
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -1,
      1
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -4,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -6,
      4
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Only desklamp is currently in FOV; encode it in the agent-centric local frame with an approximate integer position consistent with “front-left, mid distance” and its observed facing.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"desklamp": {
"position": [-2, 3],
"facin...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

{
  "desklamp": {
    "position": [
      -1,
      2
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The right-side room beyond the visible door boundary corresponds to the unobserved third room; candidate points in that region (I, E, C, G, M, Q) are in unexplored space.
FINAL ANSWER:
{
"unexplored": ["C", "E", "G", "I", "M", "Q"]
}...

🗺️ Symbolic Fog Map

#################
#......##########
#J.....##########
#.A....##########
#....D.##########
#......##########
#.KHP..##########
#####*####......#
###......#......#
###.N....+......#
###..F...#.I....#
###.....B#E.....#
###......#CG.M.Q#
###...LRO########
#################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R

🤖 Predicted: C, E, G, I, M, Q

🎯 Ground Truth: L, R, O, E, G, C, J, A, D

🧠 Cognitive Map Metrics

Global:

dir: 0.894

facing: 1

pos: 0.895

overall: 0.930

Global (Full):

dir: 0.492

facing: 0.727

pos: 0.656

overall: 0.625

Local:

dir: 1

facing: 1

pos: 0.832

overall: 0.944

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 0.333

overall: 0.400

📈 Turn Metrics

node_coverage: 0.714

edge_coverage: 0.293

is_action_fail: False

step: 5

action_counts:

move: 2

rotate: 2

return: 0

observe: 5

term: 0

forced_term: 0

query: 0

information_gain: 0.597

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 6

👤 Environment Observation (click to toggle)
You jumped to blue door. You observe:
• desklamp: front-left, mid distance, facing right.
You have a maximum of 15 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
At the blue doorway I’m likely still facing the same direction as before (west), which is giving a limited view (only the desklamp). Rotate 180° to face into the other side of the doorway (likely the unexplored room 3) and observe to reveal the remaining unseen objects....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single global integer-coordinate map consistent with all relative observations, keeping the initial pose as origin and converting each object’s reported facing into global cardinal directions based on the agent’s facing at the time of observation.
FINAL ANSWER:
```json
{
"agent": {...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      1,
      3
    ]
  },
  "blue door": {
    "position": [
      -3,
      5
    ]
  },
  "table": {
    "position": [
      5,
      3
    ]
  },
  "bike": {
    "position": [
      5,
      4
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      3,
      4
    ],
    "facing": "west"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "laptop": {
    "position": [
      -3,
      0
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -1,
      1
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -4,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -6,
      4
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: I will encode only what is currently in view (green door) in the agent-centered local frame, using an integer coordinate consistent with its front-right and slightly-far relation.
FINAL ANSWER: {
"origin": "agent",
"objects": {
"green door": {
"position": [3, 5]
}
}
}...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green door": {
      "position": [
        3,
        5
      ]
    }
  }
}

🎯 Ground Truth

{
  "green door": {
    "position": [
      2,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green door": {
      "position": [
        3,
        5
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The only clearly unobserved region is the area to the right of the single visible door (“+”), i.e., the separate right-hand room; the candidate points inside it are E, L, M, J, P, N.
FINAL ANSWER:
{"unexplored":["E","E","J","L","M","N","P"]}...

🗺️ Symbolic Fog Map

#################
#.....B##########
#..F...##########
#.....O##########
#.H....##########
#......##########
#..CR..##########
#####*####......#
###......#......#
###A.....+..EL..#
###I.....#..M...#
###......#J.....#
###....Q.#PN....#
###G..KD.########
#################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R

🤖 Predicted: E, E, J, L, M, N, P

🎯 Ground Truth: D, Q, K, N, J, P, O, F, B

🧠 Cognitive Map Metrics

Global:

dir: 0.864

facing: 1

pos: 0.883

overall: 0.916

Global (Full):

dir: 0.475

facing: 0.727

pos: 0.648

overall: 0.617

Local:

dir: 1

facing: 1

pos: 0.832

overall: 0.944

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 0.333

overall: 0.400

📈 Turn Metrics

node_coverage: 0.714

edge_coverage: 0.293

is_action_fail: False

step: 6

action_counts:

move: 2

rotate: 3

return: 0

observe: 6

term: 0

forced_term: 0

query: 0

information_gain: 0.597

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 7

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• green door: front-right, slightly far, on front wall.
You have a maximum of 14 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
From the blue doorway, current view (facing into the other side) only reveals the green door, suggesting my 90° FOV is not aimed into the interior of the next room. Rotate 90° to sweep a different sector from the same doorway position and observe, aiming to uncover the remaining unseen objects (airp...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a consistent integer-coordinate global map from the egocentric observations (using initial pose as origin, +y north), tracking agent moves/rotations and converting object-facing from relative (left/right/forward/backward) into global cardinal directions at first observation.
FINAL ANSWE...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "south"
  },
  "green door": {
    "position": [
      1,
      3
    ]
  },
  "blue door": {
    "position": [
      -3,
      5
    ]
  },
  "bike": {
    "position": [
      5,
      4
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      3,
      1
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      4
    ],
    "facing": "west"
  },
  "table": {
    "position": [
      5,
      3
    ]
  },
  "laptop": {
    "position": [
      -4,
      0
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -6,
      2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map for only the currently visible objects, assigning integer coordinates consistent with the reported bearing/distance bins and including each object's facing in local axes.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"laptop": {
"position": [2...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "laptop": {
      "position": [
        2,
        6
      ],
      "facing": "+y"
    },
    "pan": {
      "position": [
        -2,
        6
      ],
      "facing": "+y"
    },
    "teddybear": {
      "position": [
        3,
        5
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "pan": {
    "position": [
      -1,
      4
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      1,
      6
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      2,
      4
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "laptop": {
      "position": [
        2,
        6
      ],
      "facing": "+y"
    },
    "pan": {
      "position": [
        -2,
        6
      ],
      "facing": "+y"
    },
    "teddybear": {
      "position": [
        3,
        5
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: Unobserved space is likely the right-side region beyond the central door (+), where we have not seen any of the remaining objects; candidates B, E, G, I, J, L lie there.
FINAL ANSWER:
```json
{
"unexplored": ["B", "E", "G", "I", "J", "L"]
}
```...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

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

🤖 Predicted: B, E, G, I, J, L

🎯 Ground Truth: J, G, L, F, D, C

🧠 Cognitive Map Metrics

Global:

dir: 0.955

facing: 1

pos: 0.895

overall: 0.950

Global (Full):

dir: 0.525

facing: 0.727

pos: 0.656

overall: 0.636

Local:

dir: 0.833

facing: 1

pos: 0.808

overall: 0.880

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 0.500

overall: 0.500

📈 Turn Metrics

node_coverage: 0.714

edge_coverage: 0.366

is_action_fail: False

step: 7

action_counts:

move: 2

rotate: 4

return: 0

observe: 7

term: 0

forced_term: 0

query: 0

information_gain: 0.610

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 8

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• laptop: front-slight-right, slightly far, facing forward
• pan: front-slight-left, slightly far, facing forward
• teddybear: front-right, slightly far, facing backward.
You have a maximum of 13 exploration steps left.
Strictly follow this format:
THINK:
[Yo...

🤔 Assistant Thinking (click to toggle)
We still haven’t found 4 objects (airplane, lego man, candle, fire extinguisher), so we likely haven’t gotten a clear view into the third room yet. From the blue doorway, I’ll rotate 180° to face the opposite direction (a new 90° sector) and observe, to try to catch the interior of the unexplored ro...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a consistent integer-coordinate global map (origin at initial agent position), including agent (current at blue door), all observed objects/doors, and cardinal facings where provided. I’ll place doors and objects to satisfy the observed relative relationships approximately and include al...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      5
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      2,
      3
    ]
  },
  "blue door": {
    "position": [
      -2,
      5
    ]
  },
  "laptop": {
    "position": [
      -1,
      0
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      1
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -2,
      2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "table": {
    "position": [
      6,
      3
    ]
  },
  "bike": {
    "position": [
      6,
      4
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      5,
      5
    ],
    "facing": "west"
  },
  "truck": {
    "position": [
      5,
      1
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      -1,
      10
    ],
    "facing": "south"
  },
  "lego man": {
    "position": [
      -6,
      10
    ],
    "facing": "north"
  },
  "candle": {
    "position": [
      -6,
      11
    ]
  },
  "fire extinguisher": {
    "position": [
      0,
      8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) map using integer coordinates consistent with the observed bearing/distance bins; include only objects currently in FOV and add facing where provided.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"airplane": { "position": [1, 6], "facing": "+x...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "airplane": {
      "position": [
        1,
        6
      ],
      "facing": "+x"
    },
    "lego man": {
      "position": [
        -2,
        5
      ],
      "facing": "+y"
    },
    "candle": {
      "position": [
        -2,
        7
      ]
    },
    "fire extinguisher": {
      "position": [
        2,
        3
      ],
      "facing": "+y"
    }
  }
}

🎯 Ground Truth

{
  "lego man": {
    "position": [
      -3,
      4
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      1,
      6
    ],
    "facing": "east"
  },
  "candle": {
    "position": [
      -3,
      6
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      1,
      2
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "airplane": {
      "position": [
        1,
        6
      ],
      "facing": "+x"
    },
    "lego man": {
      "position": [
        -2,
        5
      ],
      "facing": "+y"
    },
    "candle": {
      "position": [
        -2,
        7
      ]
    },
    "fire extinguisher": {
      "position": [
        2,
        3
      ],
      "facing": "+y"
    }
  }
}

🎯 Ground Truth

{
  "lego man": {
    "position": [
      -3,
      4
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      1,
      6
    ],
    "facing": "east"
  },
  "candle": {
    "position": [
      -3,
      6
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      1,
      2
    ],
    "facing": "north"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A-F all lie within side regions of the right-side room that were not fully covered by our limited 90° FOV observations from the doorway positions, so they correspond to potentially unobserved areas.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

#################
#......##########
#......##########
#......##########
#......##########
#......##########
#......##########
#####*####......#
###......#CA....#
###......+......#
###......#.B....#
###......#E.....#
###......#FD....#
###......########
#################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: E, D, F

🧠 Cognitive Map Metrics

Global:

dir: 0.733

facing: 0.909

pos: 0.812

overall: 0.818

Global (Full):

dir: 0.733

facing: 0.909

pos: 0.812

overall: 0.818

Local:

dir: 0.700

facing: 1

pos: 0.852

overall: 0.851

Local (Newly):

dir: 0.700

facing: 1

pos: 0.852

overall: 0.851

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 1

edge_coverage: 0.463

is_action_fail: False

step: 8

action_counts:

move: 2

rotate: 5

return: 0

observe: 8

term: 0

forced_term: 0

query: 0

information_gain: 0.765

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 9

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• airplane: front-slight-right, slightly far, facing right
• lego man: front-left, slightly far, facing forward
• candle: front-left, slightly far
• fire extinguisher: front-right, mid distance, facing forward
Assume your current facing direction is called "...

🤔 Assistant Thinking (click to toggle)
All 12 target objects (laptop, bike, airplane, pan, truck, lego man, teddybear, desklamp, chair, candle, table, fire extinguisher) plus both doors have now been observed across the connected rooms. Further Observes would be redundant; Queries aren’t necessary unless forced to output exact coordinate...

💬 Assistant Action
Actions: [Term()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will output an approximate but internally consistent global map (integer grid) using the initial pose as origin, converting observed relative facings into global cardinal directions based on the agent orientation at the time each object was observed.
FINAL ANSWER:
```json
{
"agent": { "p...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -3,
      6
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      2,
      3
    ]
  },
  "blue door": {
    "position": [
      -3,
      6
    ]
  },
  "bike": {
    "position": [
      6,
      4
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      5,
      2
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      5,
      4
    ],
    "facing": "west"
  },
  "table": {
    "position": [
      8,
      4
    ]
  },
  "laptop": {
    "position": [
      -2,
      5
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -1,
      4
    ],
    "facing": "south"
  },
  "teddybear": {
    "position": [
      -2,
      4
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -4,
      8
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "lego man": {
    "position": [
      -5,
      11
    ],
    "facing": "north"
  },
  "candle": {
    "position": [
      -5,
      12
    ]
  },
  "fire extinguisher": {
    "position": [
      0,
      8
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "laptop": {
    "position": [
      -4,
      -1
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      6,
      5
    ],
    "facing": "south"
  },
  "airplane": {
    "position": [
      -2,
      11
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      1
    ],
    "facing": "south"
  },
  "truck": {
    "position": [
      4,
      1
    ],
    "facing": "north"
  },
  "lego man": {
    "position": [
      -6,
      9
    ],
    "facing": "north"
  },
  "teddybear": {
    "position": [
      -5,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -5,
      4
    ],
    "facing": "north"
  },
  "chair": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "candle": {
    "position": [
      -6,
      11
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      6,
      3
    ],
    "facing": "east"
  },
  "fire extinguisher": {
    "position": [
      -2,
      7
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -3,
      5
    ],
    "facing": "north"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.592

facing: 1

pos: 0.702

overall: 0.765

Global (Full):

dir: 0.592

facing: 1

pos: 0.702

overall: 0.765

Local:

(none)

Local (Newly):

(none)

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 1

edge_coverage: 0.463

is_action_fail: False

step: 9

action_counts:

move: 2

rotate: 5

return: 0

observe: 8

term: 1

forced_term: 0

query: 0

information_gain: 0.765

reward: -0.100

is_done: True

is_valid_action: True

🔄 FB Turn 10

👤 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)
To detect the 4 changed objects in the initial room (room 1) with minimal cost, I should re-observe room 1 from the same informative vantage point used before: the green doorway while facing into room 1 (west). This should reveal laptop, pan, teddybear, and desklamp again so I can compare their posi...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need to output a complete global JSON cognitive map with integer coordinates (initial agent at origin), including agent, all objects, and both doors; include facing for any entity with known facing.
FINAL ANSWER:
{
"agent": { "position": [2, 3], "facing": "west" },
"green door": { "positi...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: fire extinguisher:

dir: None

pos: 1

facing: None

overall: None

Retention: fire extinguisher:

dir: None

pos: 0.370

facing: None

overall: None

Changed: desklamp:

dir: None

pos: None

facing: 0

overall: None

Retention: desklamp:

dir: None

pos: None

facing: 0

overall: None

Unchanged (all):

dir: 0.621

facing: 0.571

pos: 0.728

overall: 0.640

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): ['desklamp', 'fire extinguisher']

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: ['desklamp', 'fire extinguisher']

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 1

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['fire extinguisher', 'desklamp']

newly_observed_unchanged_objects: ['laptop', 'pan', 'blue door']

🔄 FB Turn 11

👤 Environment Observation (click to toggle)
You jumped to green door. You rotated counterclockwise 90°. You observe:
• laptop: front-left, slightly far, facing left
• pan: front-left, mid distance, facing left
• desklamp: front-slight-right, slightly far, facing left
• fire extinguisher: front-left, slightly far, facing right
• blue door: fr...

🤔 Assistant Thinking (click to toggle)
We’ve already detected two strong anomalies in room 1: (1) fire extinguisher now appears here (previously it was in room 3) → position change, and (2) desklamp’s facing flipped (right → left) → orientation change. We still need 2 more changed objects in room 1, likely including the missing teddybear...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single consistent global grid map (origin at initial agent pose), using the qualitative distance/bearing observations to assign approximate integer coordinates and cardinal facings for all entities.
FINAL ANSWER:
```json
{
"agent": { "position": [1, 3], "facing": "north" },
"...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.894

facing: 1

pos: 0.889

overall: 0.928

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 2

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 12

👤 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)
The previous informative view was when facing into room 1 (after Rotate(-90)). After rotating back to global north, the FOV likely aligned along the doorway/wall, yielding no objects. Next, rotate 180° from the current facing to look into a different sector (global south) from the same doorway and o...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will output a single, self-consistent global grid map (integer coordinates) for all objects/doors plus the agent in the current believed configuration, using the initial position as origin and cardinal facings.
FINAL ANSWER:
```json
{
"agent": { "position": [3, 2], "facing": "south" },
...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.758

facing: 0.286

pos: 0.749

overall: 0.597

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 3

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 13

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe: No objects in field of view..
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)
We need two more changed objects in the initial room. From the green doorway, my current facing (south) isn’t giving visibility; rotate back to the earlier useful heading (west, into room 1) to regain sight of room-1 objects, then we can jump deeper into the room to check for missing/moved items lik...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will output a single consistent global JSON cognitive map (agent at origin facing north) containing all objects and both doors, using integer coordinates and cardinal facings where applicable.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "north" },
"green door": { "position"...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.667

facing: 0.857

pos: 0.739

overall: 0.754

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 4

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 14

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• laptop: front-left, slightly far, facing left
• pan: front-left, mid distance, facing left
• desklamp: front-slight-right, slightly far, facing left
• fire extinguisher: front-left, slightly far, facing right
• blue door: front-right, slightly far, on right...

🤔 Assistant Thinking (click to toggle)
From the doorway, the teddybear (previously visible in room 1) is now missing from this view, suggesting it moved elsewhere in the room. To find the remaining changed objects efficiently, jump to a visible interior landmark (laptop) to get a new vantage within room 1 and observe for additional/missi...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must output a complete global JSON map (agent, all objects, both doors) in the initial-origin frame. No further sensing is allowed here, so I’ll provide a single consistent integer-coordinate layout that fits all previously observed qualitative bearings/distances, including the currently re...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.848

facing: 1

pos: 0.803

overall: 0.884

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 5

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 15

👤 Environment Observation (click to toggle)
You jumped to laptop. You observe: No objects in field of view..
You have a maximum of 10 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
After jumping, my orientation stayed the same, and the 90° FOV is likely pointed away from the rest of the room (hence nothing visible). Rotate 90° to sweep the room interior from the laptop position and observe to locate the missing teddybear and any other altered objects....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build an approximate global integer-coordinate map anchored at the initial origin, using all previously observed objects/doors and the most recent (post-change) orientations; unobserved exact placements are estimated for completeness.
FINAL ANSWER: {
"agent": {
"position": [
-3,
...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

(none)

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: []

🧭 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': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', '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:
• pan: front-right, mid distance, facing backward
• desklamp: front-slight-left, slightly far, facing backward
• fire extinguisher: front-right, mid distance, facing forward
• blue door: front-slight-right, slightly far, on front wall
Assume your current faci...

🤔 Assistant Thinking (click to toggle)
From the laptop position, I still don’t see the teddybear (previously in room 1) or any other potential moved-in object besides the fire extinguisher. I should sweep the remaining unseen sector of room 1 by rotating 90° and observing again to locate the missing teddybear and identify the 4th changed...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single globally-referenced integer-coordinate map consistent with the previously observed door placements and object-relative bearings/proximities, including all objects, doors, and the agent at the initial origin.
FINAL ANSWER:
```json
{
"agent": { "position": [0, 0], "facing":...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.848

facing: 0.857

pos: 0.835

overall: 0.847

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 7

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: ['green door']

🔄 FB Turn 17

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• pan: front-left, mid distance, facing right
• green door: front-left, slightly far, on front wall.
You have a maximum of 8 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
From the laptop position I’ve only covered part of the room; the missing teddybear (previously in room 1) and any other newly moved-in object could be in the remaining sector. Rotate another 90° to complete the sweep from this spot and Observe()....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I’ll provide a single globally-referenced grid map (origin at initial agent pose), including all objects and both doors with approximate integer coordinates consistent with the observed multi-room structure.
FINAL ANSWER: {
"agent": { "position": [0, 0], "facing": "north" },
"green door":...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.576

facing: 0.571

pos: 0.711

overall: 0.619

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 8

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 18

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

🤔 Assistant Thinking (click to toggle)
I’m currently at the laptop facing south; the last sweep showed nothing, meaning the remaining unseen area (where the missing teddybear or another changed object could be) is likely outside the current FOV. Rotate 90° to face west (a new sector from this position) and observe again to try to locate ...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single globally consistent integer-coordinate map (origin at initial agent pose) that satisfies the observed door placements and relative room adjacency; assign plausible coordinates/orientations for all objects, updating with the newly observed changes (fire extinguisher now in r...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.667

facing: 1

pos: 0.607

overall: 0.758

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 9

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 19

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe: No objects in field of view..
You have a maximum of 6 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
We may be missing additional changed objects outside this current view. From the laptop position I currently have an empty FOV, so I’ll rotate 180° to bring previously seen landmarks/doors back into view and re-establish orientation for navigating to the other rooms to check for remaining changes....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will output a single consistent global coordinate map (origin at initial agent position) that includes the agent, all objects, and both doors, using cardinal facings where applicable.
FINAL ANSWER:
```json
{
"agent": { "position": [-3, 0], "facing": "east" },
"green door": { "position":...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.742

facing: 1

pos: 0.815

overall: 0.853

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 10

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 20

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• pan: front-left, mid distance, facing right
• green door: front-left, slightly far, on front wall.
You have a maximum of 5 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
We still need 2 more changed objects. The teddybear is missing from room 1, so it likely moved to another room. From my current spot I can see the green door; jumping back to that doorway and observing (without extra rotations first) is the fastest way to check the adjacent room for the missing tedd...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single consistent global integer-coordinate map (origin at initial agent), using observed door locations as anchors and placing objects to satisfy the qualitative bearings/distances and noted proximity constraints; include current agent pose.
FINAL ANSWER:
```json
{
"agent": { "pos...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.742

facing: 0.714

pos: 0.635

overall: 0.697

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 11

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': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: ['truck', 'chair', 'bike', 'table']

🔄 FB Turn 21

👤 Environment Observation (click to toggle)
You jumped to green door. You observe:
• bike: front-slight-left, slightly far, facing right
• truck: front-right, mid distance, facing left
• chair: front-left, mid distance, facing backward
• table: front, slightly far
Assume your current facing direction is called "north":
• bike is north, near ...

🤔 Assistant Thinking (click to toggle)
We have confirmed changes: desklamp orientation flipped (was facing right, now facing left) and fire extinguisher has moved into room 1 (previously in room 3). The teddybear is missing from its prior room-1 view, so it likely moved (position change). We still need one more changed object; to avoid g...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must output a complete global JSON cognitive map (agent, all objects, both doors) with integer coordinates relative to the initial origin and cardinal facings where applicable; since exact metric coordinates were never queried, I’ll provide a consistent approximate layout matching the multi...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.379

facing: 1

pos: 0.568

overall: 0.649

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 12

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': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 22

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• laptop: front-left, slightly far, facing left
• pan: front-left, mid distance, facing left
• desklamp: front-slight-right, slightly far, facing left
• fire extinguisher: front-left, slightly far, facing right
• blue door: front-right, slightly far, on righ...

🤔 Assistant Thinking (click to toggle)
We’ve confirmed two changes in room 1 (fire extinguisher now here; desklamp orientation flipped) and strongly suspect teddybear moved (missing). We still need the 4th changed object, so I should change vantage within room 1: jump to the blue door (visible) and observe from there to catch any additio...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will output a consistent global integer-coordinate map (origin at initial agent position, +y is north) including all objects and both doors, with estimated positions consistent with the observed qualitative bearings/distances and key proximity constraints.
FINAL ANSWER:
{
"agent": { "posi...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.682

facing: 0.857

pos: 0.751

overall: 0.763

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 13

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': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 23

👤 Environment Observation (click to toggle)
You jumped to blue door. You observe:
• desklamp: front-left, mid distance, facing left.
You have a maximum of 2 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
We still need to identify the 4th changed object. From the blue doorway, rotating 180° should let me see into the other connected room (room 3) to check whether any of airplane/lego man/candle (or other) have moved/rotated, since the fire extinguisher has already moved out....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Building a consistent approximate global coordinate map (origin at initial agent position) including all objects/doors and their best-known facings from observations.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "north" },

"green door": { "position": [4, 2] },
"blue door": ...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.409

facing: 0.143

pos: 0.598

overall: 0.383

🧾 False Belief Object Sets

all_objects: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

all_changed_objects: ['airplane', 'desklamp', 'fire extinguisher', 'teddybear']

newly_observed_changed (this turn): []

unchanged_objects: ['bike', 'blue door', 'candle', 'chair', 'green door', 'laptop', 'lego man', 'pan', 'table', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'bike', 'blue door', 'candle', 'chair', 'desklamp', 'fire extinguisher', 'green door', 'laptop', 'lego man', 'pan', 'table', 'teddybear', 'truck']

🧭 False Belief Info

step: 14

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': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 24

👤 Environment Observation (click to toggle)
You rotated clockwise 180°. You observe:
• green door: front-right, slightly far, on front wall.
You have a maximum of 1 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
From repeated doorway observations in the same viewing configuration as the original map, three changes are clearly supported: fire extinguisher is now in room 1 (position change), desklamp’s facing flipped (orientation change), and the teddybear is no longer where it was previously visible in room ...

💬 Assistant Action
Actions: [Term(changes="fire extinguisher: position, desklamp: orientation, teddybear: position, pan: position")]

📈 Turn Metrics

reward: 0.750

is_done: True

is_valid_action: True

success: 0.750

🧭 False Belief Info

step: 15

correctly_identified_changes: 0.750

f1_overall: 0.750

f1_position: 0.800

f1_facing: 0.667

action_cost: 14

action_cost_after_seen: None

ground_truth_changes: [{'name': 'airplane', 'ori': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'fire extinguisher', 'pos': True}]

reported_changes: [{'name': 'fire extinguisher', 'pos': True}, {'name': 'desklamp', 'ori': True}, {'name': 'teddybear', 'pos': True}, {'name': 'pan', 'pos': True}]

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

📊 Task: DirectionEvaluationTask

Question 1 (ID: ca87148432a7d552)

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

🤔 Assistant Answer (click to toggle)
THINK:
From the blue door viewpoint, laptop was roughly south of it and airplane roughly north of it, both at slightly-far distances from the door, so airplane is approximately north of laptop with a combined separation in the 8–16 range.
FINAL ANSWER:
north, far...

✅ Evaluation Results

id: ca87148432a7d552

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

Evaluation state — Q1: DirectionEvaluationTask

Question 2 (ID: e031d760b96a5da9)

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

🤔 Assistant Answer (click to toggle)
THINK:
Chair was observed to the northeast of the green door, while pan was observed to the southwest of the green door, so chair is northeast of pan; their combined offsets suggest a slightly-far separation.
FINAL ANSWER:
north-east, slightly far...

✅ Evaluation Results

id: e031d760b96a5da9

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

Question 3 (ID: f91d7ef70302b179)

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

🤔 Assistant Answer (click to toggle)
THINK:
Using the blue door as reference: pan is to the south-east of it, while lego man is to the north-west of it, so lego man is north-west of pan. With both being slightly far from the door in opposite directions, their separation falls in the far bin.
FINAL ANSWER:
north-west, far...

✅ Evaluation Results

id: f91d7ef70302b179

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

action: None

answer: north west, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: PovEvaluationTask

Question 1 (ID: b39725205f4d5931)

❓ Evaluation Question (click to toggle)
Now you jump to teddybear'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 green door view, desklamp was on the opposite side (right) compared to teddybear (left) at similar range, so from teddybear it lies roughly ahead when facing teddybear’s direction.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: b39725205f4d5931

question: Now you jump to teddybear'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, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: ddaba1a0c4c8aaed)

❓ Evaluation Question (click to toggle)
Now you jump to pan'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:
From prior observations, laptop lay to the right of pan at a moderate separation; aligning to pan’s facing, laptop is mainly to my right at about mid distance.
FINAL ANSWER:
right, mid distance...

✅ Evaluation Results

id: ddaba1a0c4c8aaed

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

action: None

answer: front-right, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 3 (ID: 583f1b341e5291a0)

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

🤔 Assistant Answer (click to toggle)
THINK:
From earlier, table was east of the bike (bike west of table) at near distance. Now at the bike and facing the bike’s direction (east), the table is straight ahead.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 583f1b341e5291a0

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

action: None

answer: front, near

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 38b08b9cd49edf9d)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that desklamp is front, mid distance, facing forward; blue 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:
Desklamp is seen directly in front and is facing the same way as me (“facing forward”), so my current facing matches the desklamp’s absolute facing. From earlier observations, the desklamp faces global north; therefore I’m facing global north, meaning I’m standing at an object that also faces...

✅ Evaluation Results

id: 38b08b9cd49edf9d

question: Now you jump to an object's position, facing its direction. You observe that desklamp is front, mid distance, facing forward; blue 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: teddybear

final_pos: [3, 3]

final_ori: [0, 1]

final_observation: [{'name': 'desklamp', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'blue door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 1

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

Question 2 (ID: 593eaa3f0e5ee139)

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

🤔 Assistant Answer (click to toggle)
THINK:
Facing the object’s direction, chair is straight ahead while bike and table are ahead-right, with bike farther; this matches being at the truck (which faces left), with chair due west and bike/table northwest at increasing distances.
FINAL ANSWER:
truck...

✅ Evaluation Results

id: 593eaa3f0e5ee139

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

action: None

answer:

answer: truck

final_pos: [12, 3]

final_ori: [0, 1]

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

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 2

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

Question 3 (ID: 8478901165ea117a)

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

🤔 Assistant Answer (click to toggle)
THINK:
The agent is at an object with a known facing direction. Candle has no facing; airplane faces right; fire extinguisher faces forward. From the fire extinguisher’s forward-facing pose, airplane can plausibly appear straight ahead at mid distance while candle stays front-left and slightly farth...

✅ Evaluation Results

id: 8478901165ea117a

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

action: None

answer:

answer: fire extinguisher

final_pos: [6, 9]

final_ori: [0, 1]

final_observation: [{'name': 'airplane', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}, {'name': 'candle', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': None}]

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 21808bda9b228db0)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Infer which objects are jumped to from the described ego-relations, then compute final pose at the green door facing west and locate the teddybear relative to that pose. From that final pose, teddybear lies ahead and slightly to the left at a distance in (4,8].
FINAL ANSWER:
front-slight-left...

✅ Evaluation Results

id: 21808bda9b228db0

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

action: None

answer: front-slight-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: ab43fa08726d1772)

❓ 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-slight-right, slightly far.
3. Rotate(90)
4. Jump to the object at front-right, slightly far.

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

🤔 Assistant Answer (click to toggle)
THINK:
After the action sequence, I jump onto the fire extinguisher itself, so it is at distance 0 relative to me (co-located). I’ll report ego direction as front with same distance.
FINAL ANSWER:
front, same distance...

✅ Evaluation Results

id: ab43fa08726d1772

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-slight-right, slightly far. 3. Rotate(90) 4. Jump to the object at front-right, slightly far. After executing the actions, what is the ego relation of fire extinguisher 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

Question 3 (ID: 604bedeabbf32688)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
From the start, the only previously observed item matching “front-right, slightly far” after rotating to face west is the blue door. From there, the scripted jumps place me directly south of the blue door by a mid distance; with my final facing east, the green door ends up straight ahead at a...

✅ Evaluation Results

id: 604bedeabbf32688

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

action: None

answer: front-slight-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 1a5402f686fdbe8e)

❓ 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:
candle is at front, near

What action sequence led to this final view? The action sequence must be valid and only contain move...

🤔 Assistant Answer (click to toggle)
THINK:
To see the candle “front, near” without observing in-between, end up at the lego man while facing north (since lego man was observed south/near to candle, meaning candle is just north of lego man). From the start (facing north), the blue door is in FOV and jumping there preserves facing; from...

✅ Evaluation Results

id: 1a5402f686fdbe8e

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: candle is at front, near 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: [2, 11]

final_ori: [0, 1]

room_id: 3

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

minimal_plan: [['jumpto', 'blue door'], ['jumpto', 'lego man']]

final_observation: [{'name': 'candle', 'direction': 'front', 'distance': 'near', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [2.0, 11.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: default

Question 2 (ID: 0240108a1a776391)

❓ 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:
bike is at front, near, facing backward; chair is at front-left, mid distance, facing left

What action sequence led to this f...

🤔 Assistant Answer (click to toggle)
THINK:
To get bike directly in front and near (and chair front-left mid), stand at the table and face north. From the start, the table isn’t in FOV, so first jump to the visible green door, rotate to bring the table into FOV, jump to it, then rotate back to north.
FINAL ANSWER:
JumpTo(green door), R...

✅ Evaluation Results

id: 0240108a1a776391

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: bike is at front, near, facing backward; chair is at front-left, mid distance, facing left What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [14, 5]

final_ori: [0, 1]

room_id: 2

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

minimal_plan: [['jumpto', 'green door'], ['rotate', 90], ['jumpto', 'table'], ['rotate', -90]]

final_observation: [{'name': 'bike', 'direction': 'front', 'distance': 'near', 'orientation': 'facing backward'}, {'name': 'chair', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [14.0, 5.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: default

Question 3 (ID: fe579174d12a0b9e)

❓ 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:
green door is at front-left, mid distance, on front wall

What action sequence led to this final view? The action sequence mus...

🤔 Assistant Answer (click to toggle)
THINK:
To make the move-only sequence valid (no JumpTo as the first action), start with a neutral rotation, then jump to the blue door; from there (keeping the same facing), the green door matches the described final view.
FINAL ANSWER:
Rotate(0), JumpTo(blue door)...

✅ Evaluation Results

id: fe579174d12a0b9e

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: green door is at front-left, mid distance, on front wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [6, 3]

final_ori: [1, 0]

room_id: 1

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

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

final_observation: [{'name': 'green door', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: False

final_pos: [5.0, 7.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: default

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 046899a176fbe951)

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

🤔 Assistant Answer (click to toggle)
THINK:
Derive a single consistent integer-coordinate layout from the qualitative bearings/distances and door connectivity, then report the requested objects’ allocentric coordinates relative to the start.
FINAL ANSWER:
(0, 12); (-4, 0); (-5, 12); (6, 3); (0, 10)...

✅ Evaluation Results

id: 046899a176fbe951

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

action: None

answer: [[-2, 11], [-4, -1], [-6, 11], [6, 3], [-2, 7]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.864

threshold: 0.900

score: 0.864

evaluation_mode: default

Question 2 (ID: 64c2cfa4dfb804b2)

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

🤔 Assistant Answer (click to toggle)
THINK:
Exact coordinates weren’t directly queried, so I’m inferring a single consistent integer-coordinate layout that satisfies all observed bearing/distance bins and doorway relations.
FINAL ANSWER:
(3, 5); (-1, 1); (-5, 9)...

✅ Evaluation Results

id: 64c2cfa4dfb804b2

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

action: None

answer: [[4, 5], [-2, 1], [-6, 9]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.853

threshold: 0.900

score: 0.853

evaluation_mode: default

Question 3 (ID: bf689311469baa8a)

❓ 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: lego man, pan, bike, fire extinguisher, airplane.
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 the initial observation bins, then use relative bearings/distances from the green-door and blue-door viewpoints plus the given proximity constraints to pick the consistent integer coordinates for the requested objects.
FINAL ANSWER:
(-6, 10); (-2, 1); (6, 5);...

✅ Evaluation Results

id: bf689311469baa8a

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

action: None

answer: [[-6, 9], [-2, 1], [6, 5], [-2, 7], [-2, 11]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.811

threshold: 0.900

score: 0.811

evaluation_mode: default

📊 Task: RotEvaluationTask

Question 1 (ID: 79135364fb4b164f)

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

🤔 Assistant Answer (click to toggle)
THINK:
Sort the three objects by their bearings from the start (facing north) in the order encountered during a continuous counterclockwise rotation; airplane is closest to straight-ahead first, then desklamp, then laptop.
FINAL ANSWER:
airplane, desklamp, laptop...

✅ Evaluation Results

id: 79135364fb4b164f

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: 75467a0fbc94e6b7)

❓ 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: chair, laptop, truck.
List them in the exac...

🤔 Assistant Answer (click to toggle)
THINK:
Compute approximate bearings from the start: chair is in the NE sector (smaller clockwise angle), truck is more E (larger clockwise angle), laptop is in the NW sector (negative bearing → appears late in clockwise rotation).
FINAL ANSWER:
chair, truck, laptop...

✅ Evaluation Results

id: 75467a0fbc94e6b7

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: chair, laptop, 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: ['chair', 'truck', 'laptop']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 456b022cad9cba9a)

❓ 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: bike, lego man, teddybear.
List them...

🤔 Assistant Answer (click to toggle)
THINK:
Need ordering by bearings from the start (facing north) as I rotate counterclockwise: objects on the left (negative bearing) appear first; right-side objects appear later. Lego man is in the northward (room 3) direction via the blue door; teddybear is more westerly in room 1; bike is to the e...

✅ Evaluation Results

id: 456b022cad9cba9a

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: bike, lego man, teddybear. 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: ['lego man', 'teddybear', 'bike']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: RotDualEvaluationTask

Question 1 (ID: 63b9f1a78aaf3b24)

❓ 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:
airplane, desklamp, laptop

...

🤔 Assistant Answer (click to toggle)
THINK:
From the layout, the airplane is roughly north of the start, while the desklamp is to the northwest and the laptop more toward the west/southwest. Starting facing north, sweeping from north toward northwest then further toward west/southwest matches the observed order (airplane → desklamp → l...

✅ Evaluation Results

id: 63b9f1a78aaf3b24

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: airplane, desklamp, laptop 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: ea901f1db8ed2f4b)

❓ 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:
chair, truck, laptop

Based ...

🤔 Assistant Answer (click to toggle)
THINK:
From the start facing north, seeing chair then truck then laptop as objects come to the front matches sweeping bearings from northeast to southeast to west, which occurs under a clockwise rotation.
FINAL ANSWER:
clockwise...

✅ Evaluation Results

id: ea901f1db8ed2f4b

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: chair, truck, laptop 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: 1546ca0f231b289d)

❓ 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:
lego man, teddybear, bike

B...

🤔 Assistant Answer (click to toggle)
THINK:
From the start, lego man is positioned to the left of north (NW), teddybear further along to the left (more W/NW), and bike is to the right (E/NE). Seeing them in the order lego man → teddybear → bike during a full sweep from facing north matches a counterclockwise rotation.
FINAL ANSWER:
cou...

✅ Evaluation Results

id: 1546ca0f231b289d

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: lego man, teddybear, bike 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: 77fb7413d8ff5f85)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (-3, 2) and face west.
What is the egocentric relation of teddybear?

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (-3,2) facing west; teddybear is directly ahead (west) at a mid-range distance (~3).
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: 77fb7413d8ff5f85

question: Still treat your initial position as origin (0, 0) You move to (-3, 2) and face west. What is the egocentric relation of teddybear? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-left, mid distance

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 2 (ID: 3399c355c7d0785f)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (-1,0) facing west; pan is one step west and one step north of the agent, which is ahead-right in the agent’s frame and at distance √2 (near).
FINAL ANSWER:
front-right, near...

✅ Evaluation Results

id: 3399c355c7d0785f

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

action: None

answer: front-right, near

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 1c9e744fd28c4e95)

❓ Evaluation Question (click to toggle)
Treat the blue door as the new 'origin' (0, 0).
You move to (0, 3) and face north.
What is the egocentric relation of airplane?

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

🤔 Assistant Answer (click to toggle)
THINK:
Relative to the new position (0,3) facing north, the airplane remains ahead and a bit to the right, at a slightly-far distance bin.
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: 1c9e744fd28c4e95

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

action: None

answer: front-slight-right, mid distance

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 981cabb8e8f95e0e)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Use the previously inferred map coordinates for the teddybear and desklamp, then find the unique integer point where both appear in the required egocentric bearings (front-left / front-right) at mid distance while facing west.
FINAL ANSWER:
(-3, 3)...

✅ Evaluation Results

id: 981cabb8e8f95e0e

question: You move to a new location and face west. You observe: teddybear is at front-left, mid distance, facing right; desklamp is at front-right, mid distance, facing right 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: [-3, 2]

final_pos: [5, 4]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

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

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.758

raw_score: 0.758

score: 0.758

evaluation_mode: default

Question 2 (ID: 56bb180ac8571782)

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

🤔 Assistant Answer (click to toggle)
THINK:
Using the last observation with me facing west, I infer my position is consistent with being at the previously inferred green door location relative to the origin.
FINAL ANSWER:
(1, 3)...

✅ Evaluation Results

id: 56bb180ac8571782

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

action: None

answer:

coord: [-1, 0]

final_pos: [7, 2]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'pan', 'direction': 'front-right', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'teddybear', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'laptop', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.027

raw_score: 0.027

score: 0.027

evaluation_mode: default

Question 3 (ID: d06f40c11fae70c2)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: airplane is at front-slight-right, mid distance, facing right; candle is at front-left, slightly far
Treat the blue door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

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

🤔 Assistant Answer (click to toggle)
THINK:
Use the previously consistent integer placements relative to the blue door: airplane at (1,5) (front-slight-right, slightly far from the blue door) and candle at (-3,6) (front-left, slightly far). Find the agent integer coordinate (x,y) such that airplane is front-slight-right at mid distance...

✅ Evaluation Results

id: d06f40c11fae70c2

question: You move to a new location and face north. You observe: airplane is at front-slight-right, mid distance, facing right; candle is at front-left, slightly far Treat the blue 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: [0, 3]

final_pos: [5, 10]

final_ori: [0, 1]

room_id: 3

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

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

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.717

raw_score: 0.717

score: 0.717

evaluation_mode: default

📊 Task: DirectionEvaluationTask

Question 1 (ID: ca87148432a7d552)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll provide a consistent integer-coordinate global map (origin at the start, +y north) that fits the observed room/door structure and relative placements, then compute airplane’s relative cardinal direction and binned distance from laptop using that map.
FINAL ANSWER:
<cogmap>
{
"agent": {...

✅ Evaluation Results

id: ca87148432a7d552

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

action: None

answer: north, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: e031d760b96a5da9)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single global coordinate assignment consistent with all egocentric observations and proximity constraints, using the initial agent pose as origin. Then compute chair’s position relative to pan (object-to-object) and bin into the required cardinal+distance labels.

FINAL ANSWER:
<c...

✅ Evaluation Results

id: e031d760b96a5da9

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

Question 3 (ID: f91d7ef70302b179)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent global integer grid (origin at start), placing doors and objects to satisfy the observed relative bearings/distances and the few given proximity relations; then compute lego man’s direction and distance from pan using those coordinates.
FINAL ANSWER:
<cogmap>
{
...

✅ Evaluation Results

id: f91d7ef70302b179

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

action: None

answer: north west, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: b39725205f4d5931)

❓ Evaluation Question (click to toggle)
Now you jump to teddybear'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:
I’ll assign a single consistent global coordinate to each observed object/door using the initial agent pose as origin and keeping relative layouts consistent with the observed door connectivity (room1↔room2 via green door; room1↔room3 via blue door). Then, after “jumping to teddybear” (at its...

✅ Evaluation Results

id: b39725205f4d5931

question: Now you jump to teddybear'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, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 2 (ID: ddaba1a0c4c8aaed)

❓ Evaluation Question (click to toggle)
Now you jump to pan'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 must output an integer-coordinate global cognitive map (origin at initial agent position). Since no Query() actions were used, I construct a single consistent integer grid layout that satisfies the observed relative bearings/distances/proximities. Then, after “jump to pan” and adopting pan’...

✅ Evaluation Results

id: ddaba1a0c4c8aaed

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

action: None

answer: front-right, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 3 (ID: 583f1b341e5291a0)

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

🤔 Assistant Answer (click to toggle)
THINK:
I need to output a single consistent global integer-coordinate map (origin at initial agent position), including all observed objects/doors and agent. Since no Query() was used, I’ll provide a best-fit integer layout consistent with the observed egocentric relations and door-jump positions. T...

✅ Evaluation Results

id: 583f1b341e5291a0

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

action: None

answer: front, near

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 38b08b9cd49edf9d)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll place all observed items on a single global integer grid consistent with the main relative bearings/distances across doorway observations (origin at the initial agent pose). For the question, since desklamp is “front … facing forward” after jumping to an object and facing its direction, ...

✅ Evaluation Results

id: 38b08b9cd49edf9d

question: Now you jump to an object's position, facing its direction. You observe that desklamp is front, mid distance, facing forward; blue 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: teddybear

final_pos: [3, 3]

final_ori: [0, 1]

final_observation: [{'name': 'desklamp', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'blue door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 1

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 593eaa3f0e5ee139)

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

🤔 Assistant Answer (click to toggle)
THINK:
Build a consistent integer-coordinate global map (origin at initial agent position). Use the qualitative bearings/distances and the given proximity constraints (bike near chair; bike near table; lego man near candle). Then, for the final question, test which object’s position+facing would yie...

✅ Evaluation Results

id: 593eaa3f0e5ee139

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

action: None

answer:

answer: truck

final_pos: [12, 3]

final_ori: [0, 1]

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

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 2

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 8478901165ea117a)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll construct a single globally-referenced, integer-coordinate map consistent with the observations (doors as connectors; room2 to the east of the green door; room3 beyond the blue door). For the final question, the only “stand at object, face its direction” configuration that yields airplan...

✅ Evaluation Results

id: 8478901165ea117a

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

action: None

answer:

answer: fire extinguisher

final_pos: [6, 9]

final_ori: [0, 1]

final_observation: [{'name': 'airplane', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}, {'name': 'candle', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': None}]

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 21808bda9b228db0)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent global coordinate assignment (integer grid) that matches the observed bearing/distance bins and the stated proximity constraints; then simulate the given action sequence from the start to find the final pose and compute the egocentric (direction, distance-bin) re...

✅ Evaluation Results

id: 21808bda9b228db0

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

action: None

answer: front-slight-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 604bedeabbf32688)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
I need to provide a single global JSON map (integer coordinates) for all observed objects/doors plus the agent, using the initial start as origin. Then I simulate the given rotate/jump sequence from the start to find my final pose and compute the egocentric (front/left/right + distance bin) r...

✅ Evaluation Results

id: 604bedeabbf32688

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

action: None

answer: front-slight-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: ab43fa08726d1772)

❓ 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-slight-right, slightly far.
3. Rotate(90)
4. Jump to the object at front-right, slightly far.

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent global integer-coordinate map (origin at the start, +y = north) that satisfies the observed relative bearings/distances and the reported near-relations. Then simulate the given action sequence from the start to determine the final pose and compute the egocentric ...

✅ Evaluation Results

id: ab43fa08726d1772

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

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 1a5402f686fdbe8e)

❓ 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:
candle is at front, near

What action sequence led to this final view? The action sequence must be valid and only contain move...

🤔 Assistant Answer (click to toggle)
THINK:
Assign consistent integer coordinates from the relative observations (using the start as (0,0), north as +y) and convert each observed object’s reported facing into global cardinal directions. For the final-view question, move from the start to the blue doorway (to see room 3), then jump to t...

✅ Evaluation Results

id: 1a5402f686fdbe8e

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: candle is at front, near 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: [2, 11]

final_ori: [0, 1]

room_id: 3

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

minimal_plan: [['jumpto', 'blue door'], ['jumpto', 'lego man']]

final_observation: [{'name': 'candle', 'direction': 'front', 'distance': 'near', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [2.0, 11.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 0240108a1a776391)

❓ 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:
bike is at front, near, facing backward; chair is at front-left, mid distance, facing left

What action sequence led to this f...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll provide a single consistent global coordinate assignment (integers) for all observed objects/doors relative to the start (0,0), and convert observed relative facing (“left/right/forward/backward”) into global cardinal facing using the agent’s global facing at the time of each observation...

✅ Evaluation Results

id: 0240108a1a776391

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: bike is at front, near, facing backward; chair is at front-left, mid distance, facing left What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [14, 5]

final_ori: [0, 1]

room_id: 2

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

minimal_plan: [['jumpto', 'green door'], ['rotate', 90], ['jumpto', 'table'], ['rotate', -90]]

final_observation: [{'name': 'bike', 'direction': 'front', 'distance': 'near', 'orientation': 'facing backward'}, {'name': 'chair', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: False

final_pos: [14.0, 5.0]

final_ori: [0, -1]

visible_match: False

score: 0

evaluation_mode: prompt_cogmap

Question 3 (ID: fe579174d12a0b9e)

❓ 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:
green door is at front-left, mid distance, on front wall

What action sequence led to this final view? The action sequence mus...

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent integer-coordinate layout matching the observed relative bearings/distances across the three connected rooms, then find a move-only sequence from the start that places the agent so the green door lies front-left at mid distance on the (agent’s) front wall.
FINAL ...

✅ Evaluation Results

id: fe579174d12a0b9e

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: green door is at front-left, mid distance, on front wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [6, 3]

final_ori: [1, 0]

room_id: 1

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

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

final_observation: [{'name': 'green door', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [6.0, 3.0]

final_ori: [1, 0]

visible_match: True

score: 1

evaluation_mode: prompt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 046899a176fbe951)

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

🤔 Assistant Answer (click to toggle)
THINK:
We never used Query(), so exact coordinates aren’t uniquely recoverable from only binned bearings/distances. I’m providing a single internally-consistent integer grid assignment that matches all observed qualitative relations (doorway jumps, relative bearings, and “near” constraints) and usin...

✅ Evaluation Results

id: 046899a176fbe951

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

action: None

answer: [[-2, 11], [-4, -1], [-6, 11], [6, 3], [-2, 7]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.810

threshold: 0.900

score: 0.810

evaluation_mode: prompt_cogmap

Question 2 (ID: 64c2cfa4dfb804b2)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll provide a single, globally consistent integer-coordinate map that fits the observed door locations and the relative bearing/distance constraints from the observation points (origin, green door, blue door). Then I’ll read off the requested allocentric coordinates for chair, pan, and lego ...

✅ Evaluation Results

id: 64c2cfa4dfb804b2

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

action: None

answer: [[4, 5], [-2, 1], [-6, 9]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.879

threshold: 0.900

score: 0.879

evaluation_mode: prompt_cogmap

Question 3 (ID: bf689311469baa8a)

❓ 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: lego man, pan, bike, fire extinguisher, airplane.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
We never used Query(), so exact coordinates aren’t uniquely determined from binned bearings/distances. I’m providing one internally consistent integer-coordinate global map (origin at the initial agent position, +y = initial north) that satisfies all observed qualitative relations (door-to-do...

✅ Evaluation Results

id: bf689311469baa8a

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

action: None

answer: [[-6, 9], [-2, 1], [6, 5], [-2, 7], [-2, 11]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.802

threshold: 0.900

score: 0.802

evaluation_mode: prompt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 79135364fb4b164f)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll provide a single consistent integer-coordinate global map (origin at the start, +y = north) covering all observed objects and both doors. Then, using those coordinates, I’ll compute each target object’s bearing from the start and list them in the order they align with “directly ahead” du...

✅ Evaluation Results

id: 79135364fb4b164f

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: 75467a0fbc94e6b7)

❓ 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: chair, laptop, truck.
List them in the exac...

🤔 Assistant Answer (click to toggle)
THINK:
We never used Query(), so exact coordinates can’t be uniquely determined from binned bearings/distances alone. I’ll provide a single **internally consistent integer** placement that matches all observations as closely as possible (doors anchored first; room-2 objects east of green door; room-...

✅ Evaluation Results

id: 75467a0fbc94e6b7

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: chair, laptop, 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: ['chair', 'truck', 'laptop']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 456b022cad9cba9a)

❓ 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: bike, lego man, teddybear.
List them...

🤔 Assistant Answer (click to toggle)
THINK:
I must output a global JSON cognitive map with integer coordinates (origin at the agent’s initial start, facing north). Since no Query() was used, exact coordinates were not directly given; I’ll provide a single consistent integer-coordinate layout that fits the observed bearings/distances an...

✅ Evaluation Results

id: 456b022cad9cba9a

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: bike, lego man, teddybear. 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: ['lego man', 'teddybear', 'bike']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 77fb7413d8ff5f85)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (-3, 2) and face west.
What is the egocentric relation of teddybear?

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent global integer-coordinate map (approximate, since no Query() was used) that matches all observed relative bearings/distances and door connectivity. Then compute teddybear’s egocentric direction/distance from the new pose at (-3,2) facing west using the defined an...

✅ Evaluation Results

id: 77fb7413d8ff5f85

question: Still treat your initial position as origin (0, 0) You move to (-3, 2) and face west. What is the egocentric relation of teddybear? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-left, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 2 (ID: 3399c355c7d0785f)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Compile a single globally consistent integer-coordinate map (origin at initial agent position), using the observed relative directions/distances to place doors and all 12 objects. Then compute pan’s egocentric direction/distance from the new agent pose at (-1,0) facing west.
FINAL ANSWER:
<co...

✅ Evaluation Results

id: 3399c355c7d0785f

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

action: None

answer: front-right, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 1c9e744fd28c4e95)

❓ Evaluation Question (click to toggle)
Treat the blue door as the new 'origin' (0, 0).
You move to (0, 3) and face north.
What is the egocentric relation of airplane?

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll place objects on an integer grid consistent with the observed doorway connectivity (green door connects room1↔room2; blue door connects room1↔room3) and the reported relative directions/distances. Then, using the blue door as (0,0), compute airplane’s position relative to the new agent p...

✅ Evaluation Results

id: 1c9e744fd28c4e95

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

action: None

answer: front-slight-right, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 981cabb8e8f95e0e)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
We never used Query(), so I must build an internally consistent integer-coordinate map from the binned bearings/distances across viewpoints (initial origin, green-door doorway views, blue-door doorway views). I’ll anchor doors first, then place room-1 items to satisfy the green-door observati...

✅ Evaluation Results

id: 981cabb8e8f95e0e

question: You move to a new location and face west. You observe: teddybear is at front-left, mid distance, facing right; desklamp is at front-right, mid distance, facing right 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: [-3, 2]

final_pos: [5, 4]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

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

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.758

raw_score: 0.758

score: 0.758

evaluation_mode: prompt_cogmap

Question 2 (ID: 56bb180ac8571782)

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

🤔 Assistant Answer (click to toggle)
THINK:
I need an integer-coordinate global map consistent with all qualitative (binned) bearings/distances observed from the origin and from the two doorways. Since no Query() was used, multiple exact grids are possible; I’ll provide one consistent assignment and then use it to infer the new agent l...

✅ Evaluation Results

id: 56bb180ac8571782

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

action: None

answer:

coord: [-1, 0]

final_pos: [7, 2]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'pan', 'direction': 'front-right', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'teddybear', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'laptop', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: d06f40c11fae70c2)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: airplane is at front-slight-right, mid distance, facing right; candle is at front-left, slightly far
Treat the blue door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll build a single globally consistent integer-coordinate map using the initial agent pose as origin, placing the two doors first and then placing objects to satisfy the observed relative bearings/distances and the given proximity constraints (bike–chair, bike–table, lego man–candle).
For ...

✅ Evaluation Results

id: d06f40c11fae70c2

question: You move to a new location and face north. You observe: airplane is at front-slight-right, mid distance, facing right; candle is at front-left, slightly far Treat the blue 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: [0, 3]

final_pos: [5, 10]

final_ori: [0, 1]

room_id: 3

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

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

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.624

raw_score: 0.624

score: 0.624

evaluation_mode: prompt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: ca87148432a7d552)

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

🤔 Assistant Answer (click to toggle)
THINK:
Airplane is 2 units east and 12 units north of laptop, so direction is north; distance is about 12.2, which bins to far.
FINAL ANSWER:
north, far...

✅ Evaluation Results

id: ca87148432a7d552

question: You return to your starting position and face north. From a Top-Down map, describe where airplane is relative to laptop. 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 2 (ID: e031d760b96a5da9)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from pan (-2,1) to chair (4,5): dx=6 east, dy=4 north → north-east; distance sqrt(6^2+4^2)=7.21 → slightly far.
FINAL ANSWER:
north-east, slightly far...

✅ Evaluation Results

id: e031d760b96a5da9

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

Question 3 (ID: f91d7ef70302b179)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from pan (-2,1) to lego man (-6,9): (-4,+8) → northwest; distance sqrt(80)≈8.94 → far.
FINAL ANSWER:
north-west, far...

✅ Evaluation Results

id: f91d7ef70302b179

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

action: None

answer: north west, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: b39725205f4d5931)

❓ Evaluation Question (click to toggle)
Now you jump to teddybear'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 teddybear (-5,1) facing north, desklamp at (-5,4) is directly ahead by 3 units, which is mid distance.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: b39725205f4d5931

question: Now you jump to teddybear'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, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: ddaba1a0c4c8aaed)

❓ Evaluation Question (click to toggle)
Now you jump to pan'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:
Agent is at pan (-2,1) facing south; laptop is at (-4,-1), which is 2 forward and 2 right in the agent’s frame; distance √8 ≈ 2.83.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: ddaba1a0c4c8aaed

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

action: None

answer: front-right, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 583f1b341e5291a0)

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

🤔 Assistant Answer (click to toggle)
THINK:
At bike (6,5) facing south, table at (6,3) is 2 units straight ahead.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 583f1b341e5291a0

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

action: None

answer: front, near

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 38b08b9cd49edf9d)

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

🤔 Assistant Answer (click to toggle)
THINK:
From teddybear (-5,1) facing north, desklamp at (-5,4) is directly ahead at distance 3 (mid), and blue door at (-3,5) is ahead-right at distance √20≈4.47 (slightly far). Both match the observation, and desklamp’s facing (north) appears as “facing forward”.
FINAL ANSWER:
teddybear...

✅ Evaluation Results

id: 38b08b9cd49edf9d

question: Now you jump to an object's position, facing its direction. You observe that desklamp is front, mid distance, facing forward; blue 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: teddybear

final_pos: [3, 3]

final_ori: [0, 1]

final_observation: [{'name': 'desklamp', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'blue door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 1

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 593eaa3f0e5ee139)

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

🤔 Assistant Answer (click to toggle)
THINK:
Test each object as the standing point (agent at its coordinates, facing its facing). From truck (4,1) facing north: chair (4,5) is directly ahead at distance 4 (front, mid) and faces west (left). Table (6,3) is at 45° front-right, distance ~2.8 (mid). Bike (6,5) is ~26.6° front-right, distan...

✅ Evaluation Results

id: 593eaa3f0e5ee139

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

action: None

answer:

answer: truck

final_pos: [12, 3]

final_ori: [0, 1]

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

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 2

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 8478901165ea117a)

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

🤔 Assistant Answer (click to toggle)
THINK:
If I’m standing at the fire extinguisher (−2,7) facing north, the airplane at (−2,11) is straight ahead at distance 4 (mid), and the candle at (−6,11) is front-left at distance √32≈5.7 (slightly far). This matches the observation.
FINAL ANSWER:
fire extinguisher...

✅ Evaluation Results

id: 8478901165ea117a

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

action: None

answer:

answer: fire extinguisher

final_pos: [6, 9]

final_ori: [0, 1]

final_observation: [{'name': 'airplane', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}, {'name': 'candle', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': None}]

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 21808bda9b228db0)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
After the rotations and jumps, I end at the green door at (1,3) facing west. The teddybear at (-5,1) is 6 units forward and 2 units to my left, which is in the front-slight-left bin, at distance √40 ≈ 6.32 (slightly far).
FINAL ANSWER:
front-slight-left, slightly far...

✅ Evaluation Results

id: 21808bda9b228db0

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

action: None

answer: front-slight-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 604bedeabbf32688)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Track final pose after rotations/jumps, then compute green door’s bearing and distance in the agent’s final egocentric frame (facing east at (-5,1)).
FINAL ANSWER:
front-slight-left, slightly far...

✅ Evaluation Results

id: 604bedeabbf32688

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

action: None

answer: front-slight-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: ab43fa08726d1772)

❓ 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-slight-right, slightly far.
3. Rotate(90)
4. Jump to the object at front-right, slightly far.

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

🤔 Assistant Answer (click to toggle)
THINK:
After the actions, I end at the blue door at (-3, 5) facing north. The fire extinguisher is at (-2, 7), which is (dx=+1, dy=+2): this is in the front-right bin and at distance √5≈2.24 (mid distance).
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: ab43fa08726d1772

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-slight-right, slightly far. 3. Rotate(90) 4. Jump to the object at front-right, slightly far. After executing the actions, what is the ego relation of fire extinguisher 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: 1a5402f686fdbe8e)

❓ 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:
candle is at front, near

What action sequence led to this final view? The action sequence must be valid and only contain move...

🤔 Assistant Answer (click to toggle)
THINK:
From the start, first do a no-op rotation to satisfy the “no JumpTo at first step” rule, then jump to the blue door (in initial FOV). From the blue door facing north, the lego man is in FOV; jumping to it puts the candle 2 units straight ahead.
FINAL ANSWER:
Rotate(0), JumpTo(blue door), Jump...

✅ Evaluation Results

id: 1a5402f686fdbe8e

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: candle is at front, near 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: [2, 11]

final_ori: [0, 1]

room_id: 3

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

minimal_plan: [['jumpto', 'blue door'], ['jumpto', 'lego man']]

final_observation: [{'name': 'candle', 'direction': 'front', 'distance': 'near', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [2.0, 11.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 0240108a1a776391)

❓ 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:
bike is at front, near, facing backward; chair is at front-left, mid distance, facing left

What action sequence led to this f...

🤔 Assistant Answer (click to toggle)
THINK:
The described final view matches being at the table’s coordinates (6,3) while still facing north: the bike at (6,5) is directly in front at distance 2 (near) and faces south (backward), and the chair at (4,5) is front-left at mid distance and faces west (left). So the move sequence is a jump ...

✅ Evaluation Results

id: 0240108a1a776391

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: bike is at front, near, facing backward; chair is at front-left, mid distance, facing left What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [14, 5]

final_ori: [0, 1]

room_id: 2

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

minimal_plan: [['jumpto', 'green door'], ['rotate', 90], ['jumpto', 'table'], ['rotate', -90]]

final_observation: [{'name': 'bike', 'direction': 'front', 'distance': 'near', 'orientation': 'facing backward'}, {'name': 'chair', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

error: target_not_visible

score: 0

evaluation_mode: use_gt_cogmap

Question 3 (ID: fe579174d12a0b9e)

❓ 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:
green door is at front-left, mid distance, on front wall

What action sequence led to this final view? The action sequence mus...

🤔 Assistant Answer (click to toggle)
THINK:
To see the green door on the front wall, I should end up facing east (so the room’s east wall becomes my front wall). From the start, pan is not in FOV, so rotate to bring it into view, jump to pan, then rotate to face east; from pan the green door is front-left at mid distance.
FINAL ANSWER:...

✅ Evaluation Results

id: fe579174d12a0b9e

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: green door is at front-left, mid distance, on front wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [6, 3]

final_ori: [1, 0]

room_id: 1

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

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

final_observation: [{'name': 'green door', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [6.0, 3.0]

final_ori: [1, 0]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 046899a176fbe951)

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

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided ground-truth cognitive map coordinates and list them in the requested object order.
FINAL ANSWER:
(-2, 11); (-4, -1); (-6, 11); (6, 3); (-2, 7)...

✅ Evaluation Results

id: 046899a176fbe951

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

action: None

answer: [[-2, 11], [-4, -1], [-6, 11], [6, 3], [-2, 7]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 64c2cfa4dfb804b2)

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

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided reference cognitive map coordinates for the requested objects in order: chair, pan, lego man.
FINAL ANSWER:
(4, 5); (-2, 1); (-6, 9)...

✅ Evaluation Results

id: 64c2cfa4dfb804b2

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

action: None

answer: [[4, 5], [-2, 1], [-6, 9]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: bf689311469baa8a)

❓ 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: lego man, pan, bike, fire extinguisher, airplane.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided reference cognitive map coordinates and list them in the requested object order.
FINAL ANSWER:
(-6, 9); (-2, 1); (6, 5); (-2, 7); (-2, 11)...

✅ Evaluation Results

id: bf689311469baa8a

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

action: None

answer: [[-6, 9], [-2, 1], [6, 5], [-2, 7], [-2, 11]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 79135364fb4b164f)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s bearing from the origin with 0° at north and negative angles to the west. During a continuous counterclockwise rotation from 0° down to -360°, objects are encountered in order of decreasing bearing: airplane (-10.3°), desklamp (-51.3°), laptop (-104.0°).
FINAL ANSWER:
ai...

✅ Evaluation Results

id: 79135364fb4b164f

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 75467a0fbc94e6b7)

❓ 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: chair, laptop, truck.
List them in the exac...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s clockwise bearing from the agent facing north using atan2(x,y): chair ≈ 38.7°, truck ≈ 76.0°, laptop ≈ 256.0° (i.e., -104.0°). Sort by increasing clockwise bearing.
FINAL ANSWER:
chair, truck, laptop...

✅ Evaluation Results

id: 75467a0fbc94e6b7

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: chair, laptop, 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: ['chair', 'truck', 'laptop']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 456b022cad9cba9a)

❓ 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: bike, lego man, teddybear.
List them...

🤔 Assistant Answer (click to toggle)
THINK:
Compute bearings from the origin with north as 0°. CCW rotation encounters decreasing (more negative) bearings first: lego man (-33.7°), then teddybear (-78.7°), and bike appears after wraparound at -309.8°.
FINAL ANSWER:
lego man, teddybear, bike...

✅ Evaluation Results

id: 456b022cad9cba9a

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: bike, lego man, teddybear. 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: ['lego man', 'teddybear', 'bike']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 77fb7413d8ff5f85)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (-3, 2) and face west.
What is the egocentric relation of teddybear?

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

🤔 Assistant Answer (click to toggle)
THINK:
From agent (-3,2) facing west, teddybear at (-5,1) is 2 units forward (west) and 1 unit left (south), bearing ≈ -26.6° (front-left). Distance √5 ≈ 2.24 → mid distance.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: 77fb7413d8ff5f85

question: Still treat your initial position as origin (0, 0) You move to (-3, 2) and face west. What is the egocentric relation of teddybear? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-left, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 3399c355c7d0785f)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (-1,0) facing west; pan at (-2,1) is 1 unit forward (west) and 1 unit to the right (north), giving a 45° front-right bearing and distance √2 ≈ 1.41 (near).
FINAL ANSWER:
front-right, near...

✅ Evaluation Results

id: 3399c355c7d0785f

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

action: None

answer: front-right, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 1c9e744fd28c4e95)

❓ Evaluation Question (click to toggle)
Treat the blue door as the new 'origin' (0, 0).
You move to (0, 3) and face north.
What is the egocentric relation of airplane?

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

🤔 Assistant Answer (click to toggle)
THINK:
Blue door is origin. Airplane at (1,6). Agent at (0,3) facing north, so airplane relative vector is (dx=1, dy=3): small right offset and forward. Angle ≈ +18° → front-slight-right. Distance √10 ≈ 3.16 → mid distance.
FINAL ANSWER:
front-slight-right, mid distance...

✅ Evaluation Results

id: 1c9e744fd28c4e95

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

action: None

answer: front-slight-right, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 981cabb8e8f95e0e)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Using the known map coordinates for teddybear (-5,1) and desklamp (-5,4), the only integer agent positions consistent with facing west and seeing them front-left/front-right at mid distance are (-3,2) or (-3,3); choose (-3,3) as the consistent solution.
FINAL ANSWER:
(-3, 3)...

✅ Evaluation Results

id: 981cabb8e8f95e0e

question: You move to a new location and face west. You observe: teddybear is at front-left, mid distance, facing right; desklamp is at front-right, mid distance, facing right 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: [-3, 2]

final_pos: [5, 4]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

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

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.758

raw_score: 0.758

score: 0.758

evaluation_mode: use_gt_cogmap

Question 2 (ID: 56bb180ac8571782)

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

🤔 Assistant Answer (click to toggle)
THINK:
Match the observed egocentric bearings/distances (facing west) to the known object coordinates; the only integer position consistent with pan being front-right near and the other two bearings/distances is (-1, 0).
FINAL ANSWER:
(-1, 0)...

✅ Evaluation Results

id: 56bb180ac8571782

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

action: None

answer:

coord: [-1, 0]

final_pos: [7, 2]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'pan', 'direction': 'front-right', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'teddybear', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'laptop', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: d06f40c11fae70c2)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: airplane is at front-slight-right, mid distance, facing right; candle is at front-left, slightly far
Treat the blue door as the new 'origin' (0, 0).
What is your new 2D coordinate (x, y)?

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

🤔 Assistant Answer (click to toggle)
THINK:
Use ground-truth object positions. Let agent be at \((x,y)\). Airplane at \((-2,11)\) is front-slight-right and mid-distance; candle at \((-6,11)\) is front-left and slightly-far. Solving the bearing/distance constraints gives agent at \((-3,8)\) in the original map. With blue door as origin ...

✅ Evaluation Results

id: d06f40c11fae70c2

question: You move to a new location and face north. You observe: airplane is at front-slight-right, mid distance, facing right; candle is at front-left, slightly far Treat the blue 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: [0, 3]

final_pos: [5, 10]

final_ori: [0, 1]

room_id: 3

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

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

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: ca87148432a7d552)

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

🤔 Assistant Answer (click to toggle)
THINK:
Airplane and laptop share the same x-coordinate, and airplane’s y is higher by 6 units, so airplane is north of laptop at a slightly far distance.
FINAL ANSWER:
north, slightly far...

✅ Evaluation Results

id: ca87148432a7d552

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

action: None

answer: north, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 2 (ID: e031d760b96a5da9)

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

🤔 Assistant Answer (click to toggle)
THINK:
Chair is 6 units to the east of pan, so direction is east and distance bin is slightly far.
FINAL ANSWER:
east, slightly far...

✅ Evaluation Results

id: e031d760b96a5da9

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

action: None

answer: north east, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 3 (ID: f91d7ef70302b179)

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

🤔 Assistant Answer (click to toggle)
THINK: Using the map coordinates, lego man (-5,11) relative to pan (-1,4) is 4 units west and 7 units north (north-west) at distance √65 ≈ 8.1, which falls in the "far" bin.
FINAL ANSWER:
north-west, far...

✅ Evaluation Results

id: f91d7ef70302b179

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

action: None

answer: north west, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: b39725205f4d5931)

❓ Evaluation Question (click to toggle)
Now you jump to teddybear'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:
After jumping to the teddybear and facing the teddybear’s facing direction (north), the desklamp is 2 units to the left and 4 units in front, which is front-left at a slightly far distance.
FINAL ANSWER:
front-left, slightly far...

✅ Evaluation Results

id: b39725205f4d5931

question: Now you jump to teddybear'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, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: ddaba1a0c4c8aaed)

❓ Evaluation Question (click to toggle)
Now you jump to pan'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 to pan and facing south (pan’s facing), laptop at (-2,5) is 1 left (west) and 1 back (north) from me; that is back-right in my egocentric frame, at distance √2 (near).
FINAL ANSWER:
back-right, near...

✅ Evaluation Results

id: ddaba1a0c4c8aaed

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

action: None

answer: front-right, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: 583f1b341e5291a0)

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

🤔 Assistant Answer (click to toggle)
THINK:
At the bike position, facing the bike’s direction (south), the table is 2 units to the east (agent’s left), which is outside the ±45° egocentric bins, so it falls into beyond-fov; distance is 2 → near.
FINAL ANSWER:
beyond-fov, near...

✅ Evaluation Results

id: 583f1b341e5291a0

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

action: None

answer: front, near

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 38b08b9cd49edf9d)

❓ Evaluation Question (click to toggle)
Now you jump to an object's position, facing its direction.
You observe that desklamp is front, mid distance, facing forward; blue 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: The agent is at an object whose facing matches the agent’s (desklamp is “facing forward”). In room 1, the only observed object (besides the desklamp itself) that faces the same way (north) is the teddybear; from there, the desklamp can appear straight ahead with the blue door ahead-right.
FIN...

✅ Evaluation Results

id: 38b08b9cd49edf9d

question: Now you jump to an object's position, facing its direction. You observe that desklamp is front, mid distance, facing forward; blue 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: teddybear

final_pos: [3, 3]

final_ori: [0, 1]

final_observation: [{'name': 'desklamp', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'blue door', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'on front wall'}]

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 1

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 593eaa3f0e5ee139)

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

🤔 Assistant Answer (click to toggle)
THINK: The only plausible stand-point object is the truck: jumping to it (facing its direction) can make chair appear directly ahead, with table and bike in the front-right sector at increasing distances.
FINAL ANSWER:
truck...

✅ Evaluation Results

id: 593eaa3f0e5ee139

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

action: None

answer:

answer: truck

final_pos: [12, 3]

final_ori: [0, 1]

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

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 2

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 8478901165ea117a)

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

🤔 Assistant Answer (click to toggle)
THINK: Airplane facing “right” implies my facing is “north” (so airplane’s facing is east). The only plausible object I could be standing on while facing north such that airplane is directly in front at mid distance and candle is front-left slightly far is the fire extinguisher.
FINAL ANSWER:
fire e...

✅ Evaluation Results

id: 8478901165ea117a

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

action: None

answer:

answer: fire extinguisher

final_pos: [6, 9]

final_ori: [0, 1]

final_observation: [{'name': 'airplane', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing right'}, {'name': 'candle', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': None}]

object_positions:

laptop: [4.0, 1.0]

bike: [14.0, 7.0]

airplane: [6.0, 13.0]

pan: [6.0, 3.0]

truck: [12.0, 3.0]

lego man: [2.0, 11.0]

teddybear: [3.0, 3.0]

desklamp: [3.0, 6.0]

chair: [12.0, 7.0]

candle: [2.0, 13.0]

table: [14.0, 5.0]

fire extinguisher: [6.0, 9.0]

green door: [9.0, 5.0]

blue door: [5.0, 7.0]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 21808bda9b228db0)

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

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

🤔 Assistant Answer (click to toggle)
THINK: Execute the action sequence to end at the green door facing west; then compute teddybear’s relative bearing and distance from that final pose.
FINAL ANSWER:
front-slight-left, slightly far...

✅ Evaluation Results

id: 21808bda9b228db0

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

action: None

answer: front-slight-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 604bedeabbf32688)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
From the start, after Rotate(-90) (facing west), the only previously observed item at front-right & slightly far is the blue door, so the first jump is to the blue door. The subsequent scripted jump lands me at a location southeast of the green door; after the final Rotate(-90) I’m facing eas...

✅ Evaluation Results

id: 604bedeabbf32688

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

action: None

answer: front-slight-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 3 (ID: ab43fa08726d1772)

❓ 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-slight-right, slightly far.
3. Rotate(90)
4. Jump to the object at front-right, slightly far.

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

🤔 Assistant Answer (click to toggle)
THINK:
From start facing north, after Rotate(-90) (facing west) the object at front-slight-right, slightly far is the blue door. From the blue door, after Rotate(90) (facing north), the object at front-right, slightly far is the airplane. Relative to the airplane (still facing north), the fire extin...

✅ Evaluation Results

id: ab43fa08726d1772

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-slight-right, slightly far. 3. Rotate(90) 4. Jump to the object at front-right, slightly far. After executing the actions, what is the ego relation of fire extinguisher 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: 0.500

evaluation_mode: use_model_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 1a5402f686fdbe8e)

❓ 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:
candle is at front, near

What action sequence led to this final view? The action sequence must be valid and only contain move...

🤔 Assistant Answer (click to toggle)
THINK:
To make candle appear front and near without observing mid-way, start with a rotate (required), jump to a visible door from the start, then jump to an object near the candle (lego man) while keeping north-facing so the candle is directly ahead.
FINAL ANSWER:
Rotate(0), JumpTo(blue door), Jump...

✅ Evaluation Results

id: 1a5402f686fdbe8e

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: candle is at front, near 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: [2, 11]

final_ori: [0, 1]

room_id: 3

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

minimal_plan: [['jumpto', 'blue door'], ['jumpto', 'lego man']]

final_observation: [{'name': 'candle', 'direction': 'front', 'distance': 'near', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [2.0, 11.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 0240108a1a776391)

❓ 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:
bike is at front, near, facing backward; chair is at front-left, mid distance, facing left

What action sequence led to this f...

🤔 Assistant Answer (click to toggle)
THINK: To legally start without a jump, rotate in place, then jump to the green door, rotate to face into room 2 so the table is in view, jump to the table (near the bike), then rotate to face the bike so it appears front.
FINAL ANSWER:
Rotate(0), JumpTo(green door), Rotate(90), JumpTo(table), Rotat...

✅ Evaluation Results

id: 0240108a1a776391

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: bike is at front, near, facing backward; chair is at front-left, mid distance, facing left What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [14, 5]

final_ori: [0, 1]

room_id: 2

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

minimal_plan: [['jumpto', 'green door'], ['rotate', 90], ['jumpto', 'table'], ['rotate', -90]]

final_observation: [{'name': 'bike', 'direction': 'front', 'distance': 'near', 'orientation': 'facing backward'}, {'name': 'chair', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [14.0, 5.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: fe579174d12a0b9e)

❓ 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:
green door is at front-left, mid distance, on front wall

What action sequence led to this final view? The action sequence mus...

🤔 Assistant Answer (click to toggle)
THINK:
From the start, turning to face east would make the green door lie on the front wall and shift slightly to the left in the 90° FOV, matching “front-left, mid distance, on front wall”.
FINAL ANSWER:
Rotate(90)...

✅ Evaluation Results

id: fe579174d12a0b9e

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: green door is at front-left, mid distance, on front wall What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [6, 3]

final_ori: [1, 0]

room_id: 1

init_pos: [8, 2]

init_ori: [0, 1]

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

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

final_observation: [{'name': 'green door', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [8.0, 2.0]

final_ori: [1, 0]

visible_match: False

score: 0

evaluation_mode: use_model_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 046899a176fbe951)

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

🤔 Assistant Answer (click to toggle)
THINK:
Use the global cognitive map coordinates (origin at the agent start (0,0), x east/right, y north/up) and report the requested objects in the given order.
FINAL ANSWER:
(-2, 11); (-2, 5); (-5, 12); (8, 4); (0, 8)...

✅ Evaluation Results

id: 046899a176fbe951

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

action: None

answer: [[-2, 11], [-4, -1], [-6, 11], [6, 3], [-2, 7]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.715

threshold: 0.900

score: 0.715

evaluation_mode: use_model_cogmap

Question 2 (ID: 64c2cfa4dfb804b2)

❓ 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: chair, pan, lego man.
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 (origin at initial position, x east/right, y north/up) for the requested objects in order.
FINAL ANSWER:
(5, 4); (-1, 4); (-5, 11)...

✅ Evaluation Results

id: 64c2cfa4dfb804b2

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

action: None

answer: [[4, 5], [-2, 1], [-6, 9]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.612

threshold: 0.900

score: 0.612

evaluation_mode: use_model_cogmap

Question 3 (ID: bf689311469baa8a)

❓ 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: lego man, pan, bike, fire extinguisher, airplane.
Answer format: (x0, y0); (x1, y1); ... in the same order.
Example: (1, 0); (-2, 3); (0, -1)
...

🤔 Assistant Answer (click to toggle)
THINK: Use the established global cognitive map coordinates (origin at the initial position, x east/right, y north/up) and report the requested objects in the given order.
FINAL ANSWER:
(-5, 11); (-1, 4); (6, 4); (0, 8); (-2, 11)...

✅ Evaluation Results

id: bf689311469baa8a

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

action: None

answer: [[-6, 9], [-2, 1], [6, 5], [-2, 7], [-2, 11]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.693

threshold: 0.900

score: 0.693

evaluation_mode: use_model_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 79135364fb4b164f)

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

🤔 Assistant Answer (click to toggle)
THINK: Using the inferred global bearings from the start position (north-facing), airplane is at ~0° (due north), desklamp at ~-59° (northwest), and laptop at ~-117° (southwest). Rotating counterclockwise from 0° encounters them in decreasing-bearing order.
FINAL ANSWER:
airplane, desklamp, laptop...

✅ Evaluation Results

id: 79135364fb4b164f

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 75467a0fbc94e6b7)

❓ 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: chair, laptop, truck.
List them in the exac...

🤔 Assistant Answer (click to toggle)
THINK:
Use the global coordinates relative to the start to compute each object’s clockwise bearing from north (atan2(x,y)) and sort from 0° to 360°.
FINAL ANSWER:
chair, truck, laptop...

✅ Evaluation Results

id: 75467a0fbc94e6b7

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: chair, laptop, 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: ['chair', 'truck', 'laptop']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 456b022cad9cba9a)

❓ 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: bike, lego man, teddybear.
List them...

🤔 Assistant Answer (click to toggle)
THINK: Compute each object’s bearing from the origin (0,0) with 0° at north; during counterclockwise rotation bearings are encountered from 0 down to -360. Lego man (-24.4°) comes just before teddybear (-26.6°), and bike (56.3°) is encountered near the end as -303.7°.
FINAL ANSWER:
lego man, teddybe...

✅ Evaluation Results

id: 456b022cad9cba9a

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: bike, lego man, teddybear. 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: ['lego man', 'teddybear', 'bike']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 77fb7413d8ff5f85)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (-3, 2) and face west.
What is the egocentric relation of teddybear?

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

🤔 Assistant Answer (click to toggle)
THINK: Agent at (-3,2) facing west; teddybear at (-2,4) is 1 east and 2 north (mostly to agent’s right and slightly behind), giving an egocentric bearing outside ±45°, distance ≈2.24 (mid).
FINAL ANSWER:
beyond-fov, mid distance...

✅ Evaluation Results

id: 77fb7413d8ff5f85

question: Still treat your initial position as origin (0, 0) You move to (-3, 2) and face west. What is the egocentric relation of teddybear? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-left, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 2 (ID: 3399c355c7d0785f)

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

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

🤔 Assistant Answer (click to toggle)
THINK: From agent (-1,0) facing west, pan at (-1,4) is directly to the agent’s right (90°), which is outside the ±45° FOV bins, so it is beyond-fov. Distance is 4 units, which bins to mid distance.
FINAL ANSWER:
beyond-fov, mid distance...

✅ Evaluation Results

id: 3399c355c7d0785f

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

action: None

answer: front-right, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: 1c9e744fd28c4e95)

❓ Evaluation Question (click to toggle)
Treat the blue door as the new 'origin' (0, 0).
You move to (0, 3) and face north.
What is the egocentric relation of airplane?

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

🤔 Assistant Answer (click to toggle)
THINK: With blue door as (0,0), airplane is at (1,5). From agent at (0,3) facing north, relative vector to airplane is (1,2): bearing ≈ +26.6° → front-right; distance ≈ 2.24 → mid distance.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 1c9e744fd28c4e95

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

action: None

answer: front-slight-right, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 981cabb8e8f95e0e)

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

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

🤔 Assistant Answer (click to toggle)
THINK: Using prior inferred global coordinates from exploration: desklamp at (-5,4) and teddybear at (-6,1). Facing west, “front-right” means the desklamp is west-and-north of me; “front-left” means the teddybear is west-and-south of me, both at mid distance. The unique integer position satisfying t...

✅ Evaluation Results

id: 981cabb8e8f95e0e

question: You move to a new location and face west. You observe: teddybear is at front-left, mid distance, facing right; desklamp is at front-right, mid distance, facing right 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: [-3, 2]

final_pos: [5, 4]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

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

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.758

raw_score: 0.758

score: 0.758

evaluation_mode: use_model_cogmap

Question 2 (ID: 56bb180ac8571782)

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

🤔 Assistant Answer (click to toggle)
THINK: Using the relative bearings/distances (facing west), the configuration is consistent with the agent being back at the global origin.
FINAL ANSWER:
(0, 0)...

✅ Evaluation Results

id: 56bb180ac8571782

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

action: None

answer:

coord: [-1, 0]

final_pos: [7, 2]

final_ori: [-1, 0]

room_id: 1

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'pan', 'direction': 'front-right', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'teddybear', 'direction': 'front-slight-right', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'laptop', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.368

raw_score: 0.368

score: 0.368

evaluation_mode: use_model_cogmap

Question 3 (ID: d06f40c11fae70c2)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: airplane is at front-slight-right, mid distance, facing right; candle is at front-left, slightly far
Treat the blue 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 blue door as (0,0), the integer location that makes airplane appear front-slight-right at mid distance and candle front-left at slightly-far distance is (0,2).
FINAL ANSWER:
(0, 2)...

✅ Evaluation Results

id: d06f40c11fae70c2

question: You move to a new location and face north. You observe: airplane is at front-slight-right, mid distance, facing right; candle is at front-left, slightly far Treat the blue 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: [0, 3]

final_pos: [5, 10]

final_ori: [0, 1]

room_id: 3

object_positions:

laptop: [4, 1]

bike: [14, 7]

airplane: [6, 13]

pan: [6, 3]

truck: [12, 3]

lego man: [2, 11]

teddybear: [3, 3]

desklamp: [3, 6]

chair: [12, 7]

candle: [2, 13]

table: [14, 5]

fire extinguisher: [6, 9]

green door: [9, 5]

blue door: [5, 7]

object_orientations:

laptop: [0, -1]

bike: [0, -1]

airplane: [1, 0]

pan: [0, -1]

truck: [0, 1]

lego man: [0, 1]

teddybear: [0, 1]

desklamp: [0, 1]

chair: [-1, 0]

fire extinguisher: [0, 1]

green door: [1, 0]

blue door: [0, 1]

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

gate_info:

green door:

room_ids: [1, 2]

ori_by_room:

1: [-1, 0]

2: [1, 0]

blue door:

room_ids: [1, 3]

ori_by_room:

1: [0, -1]

3: [0, 1]

object_rooms:

laptop: 1

bike: 2

airplane: 3

pan: 1

truck: 2

lego man: 3

teddybear: 1

desklamp: 1

chair: 2

candle: 3

table: 2

fire extinguisher: 3

green door: [1, 2]

blue door: [1, 3]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.717

raw_score: 0.717

score: 0.717

evaluation_mode: use_model_cogmap

Model: gpt-5.2

sample_run02

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📊 Sample Metrics

🔍 Exploration

✅ Evaluation

✅ Evaluation (prompt_cogmap)

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✅ 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

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🧭 False Belief Exploration

🔄 FB Turn 10

🔄 FB Turn 11

🔄 FB Turn 12

🔄 FB Turn 13

🔄 FB Turn 14

🔄 FB Turn 15

🔄 FB Turn 16

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🔄 FB Turn 18

🔄 FB Turn 19

🔄 FB Turn 20

🔄 FB Turn 21

🔄 FB Turn 22

🔄 FB Turn 23

🔄 FB Turn 24

📊 Evaluation (default)

📊 Task: DirectionEvaluationTask

Question 1 (ID: ca87148432a7d552)

Question 2 (ID: e031d760b96a5da9)

Question 3 (ID: f91d7ef70302b179)

📊 Task: PovEvaluationTask

Question 1 (ID: b39725205f4d5931)

Question 2 (ID: ddaba1a0c4c8aaed)

Question 3 (ID: 583f1b341e5291a0)

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 38b08b9cd49edf9d)

Question 2 (ID: 593eaa3f0e5ee139)

Question 3 (ID: 8478901165ea117a)

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 21808bda9b228db0)

Question 2 (ID: ab43fa08726d1772)

Question 3 (ID: 604bedeabbf32688)

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: 1a5402f686fdbe8e)

Question 2 (ID: 0240108a1a776391)

Question 3 (ID: fe579174d12a0b9e)

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: 046899a176fbe951)

Question 2 (ID: 64c2cfa4dfb804b2)

Question 3 (ID: bf689311469baa8a)

📊 Task: RotEvaluationTask

Question 1 (ID: 79135364fb4b164f)

Question 2 (ID: 75467a0fbc94e6b7)

Question 3 (ID: 456b022cad9cba9a)

📊 Task: RotDualEvaluationTask

Question 1 (ID: 63b9f1a78aaf3b24)