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 (0,0) facing north to discover visible objects/doors and plan efficient jumps toward doorways for multi-room coverage....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: No objects were observed; cognitive map contains only the agent at the origin facing north.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "north" }
}...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth (Observed)

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

🎯 Ground Truth (Full)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: No objects or doors are currently within the 90° FOV, so the local cognitive map contains an empty objects dictionary relative 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: Initial FOV faced north; candidates south of the agent are outside observed region, while those north are likely within the observed cone.
FINAL ANSWER:
{
"unexplored": ["A", "F"]
}...

🗺️ Symbolic Fog Map

################
#########......#
#########......#
##DBC...#......#
##.....E#......#
##*.....#......#
##.....F+......#
##......########
##.A....########
######+#########
#......#########
#......#########
#......#########
#......#########
#......#########
#......#########
################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, F

🎯 Ground Truth: E, F, A

🧠 Cognitive Map Metrics

Global:

dir: 1

facing: 1

pos: 1

overall: 1

Global (Full):

dir: 0.008

facing: 0.083

pos: 0.067

overall: 0.053

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 1

recall: 0.667

overall: 0.800

📈 Turn Metrics

node_coverage: 0

edge_coverage: 0

is_action_fail: False

step: 1

action_counts:

move: 0

rotate: 0

return: 0

observe: 1

term: 0

forced_term: 0

query: 0

information_gain: 0

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 2

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

🤔 Assistant Thinking (click to toggle)
Nothing visible facing north; rotate to scan other directions. Next, face east to check that quadrant for any objects or doors....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a globally-referenced map: agent stayed at origin but is now facing east; convert locally-reported proximity directions (where local “north” = global east) into global coordinates and set integer positions consistent with distance/proximity constraints and reported object facings.
FINAL...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  },
  "shoes": {
    "position": [
      5,
      -1
    ],
    "facing": "west"
  },
  "rubberduck": {
    "position": [
      3,
      -1
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      3,
      1
    ],
    "facing": "west"
  },
  "yellow door": {
    "position": [
      7,
      -1
    ]
  },
  "blue door": {
    "position": [
      6,
      -4
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Use agent-centric integer coordinates consistent with binned bearings/distances and the given proximity constraints; include facing only for items with stated facing directions.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"shoes": {
"position": [2, 4],
"facing": "-y"
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shoes": {
      "position": [
        2,
        4
      ],
      "facing": "-y"
    },
    "rubberduck": {
      "position": [
        1,
        3
      ],
      "facing": "-x"
    },
    "alarmclock": {
      "position": [
        -1,
        3
      ],
      "facing": "-y"
    },
    "yellow door": {
      "position": [
        2,
        5
      ]
    },
    "blue door": {
      "position": [
        6,
        4
      ]
    }
  }
}

🎯 Ground Truth

{
  "alarmclock": {
    "position": [
      -1,
      2
    ],
    "facing": "west"
  },
  "shoes": {
    "position": [
      1,
      4
    ],
    "facing": "west"
  },
  "rubberduck": {
    "position": [
      1,
      2
    ],
    "facing": "north"
  },
  "blue door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "yellow door": {
    "position": [
      1,
      6
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shoes": {
      "position": [
        2,
        4
      ],
      "facing": "-y"
    },
    "rubberduck": {
      "position": [
        1,
        3
      ],
      "facing": "-x"
    },
    "alarmclock": {
      "position": [
        -1,
        3
      ],
      "facing": "-y"
    },
    "yellow door": {
      "position": [
        2,
        5
      ]
    },
    "blue door": {
      "position": [
        6,
        4
      ]
    }
  }
}

🎯 Ground Truth

{
  "alarmclock": {
    "position": [
      -1,
      2
    ],
    "facing": "west"
  },
  "shoes": {
    "position": [
      1,
      4
    ],
    "facing": "west"
  },
  "rubberduck": {
    "position": [
      1,
      2
    ],
    "facing": "north"
  },
  "blue door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "yellow door": {
    "position": [
      1,
      6
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: All candidate points A-F lie to the west/left-side region of the current room, which was outside the last 90° FOV (facing east), so they remain unobserved/unexplored.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E", "F"]
}...

🗺️ Symbolic Fog Map

################
#########......#
#########......#
##....C.#......#
##.....D#......#
##*.....#......#
##FA....+......#
##.E....########
##B.....########
######+#########
#......#########
#......#########
#......#########
#......#########
#......#########
#......#########
################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: E, F, B

🧠 Cognitive Map Metrics

Global:

dir: 0.714

facing: 1

pos: 0.867

overall: 0.860

Global (Full):

dir: 0.125

facing: 0.333

pos: 0.347

overall: 0.268

Local:

dir: 0.467

facing: 1

pos: 0.847

overall: 0.771

Local (Newly):

dir: 0.467

facing: 1

pos: 0.847

overall: 0.771

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.357

edge_coverage: 0.122

is_action_fail: False

step: 2

action_counts:

move: 0

rotate: 1

return: 0

observe: 2

term: 0

forced_term: 0

query: 0

information_gain: 0.318

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 3

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• shoes: front-slight-right, slightly far, facing backward
• rubberduck: front-right, mid distance, facing left
• alarmclock: front-left, mid distance, facing backward
• yellow door: front-slight-right, slightly far, on front wall
• blue door: front-right, sl...

🤔 Assistant Thinking (click to toggle)
Best next vantage is a doorway since it opens visibility into both connected rooms. Blue door is currently in FOV and on the right wall, so I’ll jump to it and observe to potentially reveal a second room (and maybe a third via another door)....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a consistent integer-coordinate map in the global frame (origin at initial agent position). Convert observed object facings from the agent’s then-facing (east): backward→west, left→north. Place items to satisfy distance/bearing bins and the reported near-relations.
FINAL ANSWER:
{
"ag...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      5,
      -4
    ],
    "facing": "east"
  },
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ]
  },
  "blue door": {
    "position": [
      5,
      -4
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: No objects are currently visible from the blue door in my present facing direction, so the local map contains an empty objects set.
FINAL ANSWER:
{"origin":"agent","objects":{}}...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: The candidate points A-F are all located away from the agent’s current enclosed position and lie in regions not yet directly observed/covered from the agent’s viewpoints, so they should be treated as unexplored.
FINAL ANSWER: {"unexplored":["A","B","C","D","E","F"]}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E, F

🎯 Ground Truth: C, F, B

🧠 Cognitive Map Metrics

Global:

dir: 0.810

facing: 1

pos: 0.931

overall: 0.913

Global (Full):

dir: 0.142

facing: 0.333

pos: 0.372

overall: 0.282

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.357

edge_coverage: 0.122

is_action_fail: False

step: 3

action_counts:

move: 1

rotate: 1

return: 0

observe: 3

term: 0

forced_term: 0

query: 0

information_gain: 0.318

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 4

👤 Environment Observation (click to toggle)
You jumped to blue door. You observe: No objects in field of view..
You have a maximum of 17 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
At the blue doorway but current FOV shows nothing; likely looking along/into a blank wall or missing the room interiors. Rotate to scan from the doorway; start with a 90° clockwise turn and observe to try to see into the connected room(s)....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Infer a consistent global map using the initial pose as origin; approximate integer coordinates that satisfy the egocentric direction/distance bins from observations, and update agent pose after JumpTo(blue door) and Rotate(90) (now facing south).
FINAL ANSWER:
{
"agent": { "position": [5, ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      5,
      -4
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      5,
      -1
    ],
    "facing": "west"
  },
  "rubberduck": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      3,
      2
    ],
    "facing": "west"
  },
  "yellow door": {
    "position": [
      4,
      -1
    ]
  },
  "blue door": {
    "position": [
      5,
      -4
    ]
  },
  "shoppingcart": {
    "position": [
      5,
      -9
    ],
    "facing": "south"
  },
  "red chair": {
    "position": [
      3,
      -7
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Observed)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "south"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map of only the currently visible objects, assigning approximate integer coordinates consistent with the observed bearing/distance bins; include each object's facing in local axes.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"shoppingcart": {
"...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shoppingcart": {
      "position": [
        0,
        5
      ],
      "facing": "+y"
    },
    "red chair": {
      "position": [
        2,
        3
      ],
      "facing": "+y"
    }
  }
}

🎯 Ground Truth

{
  "shoppingcart": {
    "position": [
      0,
      5
    ],
    "facing": "south"
  },
  "red chair": {
    "position": [
      2,
      3
    ],
    "facing": "south"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shoppingcart": {
      "position": [
        0,
        5
      ],
      "facing": "+y"
    },
    "red chair": {
      "position": [
        2,
        3
      ],
      "facing": "+y"
    }
  }
}

🎯 Ground Truth

{
  "shoppingcart": {
    "position": [
      0,
      5
    ],
    "facing": "south"
  },
  "red chair": {
    "position": [
      2,
      3
    ],
    "facing": "south"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: Most explored so far is around the blue doorway and the immediately visible area beyond it; the left-side and lower-left floor pockets (with A, F, C, K, J, E, D, H, I, L, G) appear separated by walls and remain unobserved.
FINAL ANSWER:
{
"unexplored": ["A", "C", "D", "E", "F", "G", "H", "...

🗺️ Symbolic Fog Map

################
#########......#
#########......#
##.CK...#......#
##......#......#
##......#......#
##J..B..+......#
##......########
##AF....########
######*#########
#.EDH..#########
#....I.#########
#......#########
#......#########
#..L..G#########
#......#########
################

🔍 Fog Probe JSONs

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

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

🎯 Ground Truth: F, A, J, H, E, D

🧠 Cognitive Map Metrics

Global:

dir: 0.778

facing: 1

pos: 0.854

overall: 0.877

Global (Full):

dir: 0.233

facing: 0.500

pos: 0.455

overall: 0.396

Local:

dir: 1

facing: 1

pos: 1

overall: 1

Local (Newly):

dir: 1

facing: 1

pos: 1

overall: 1

Fog Probe:

precision: 0.545

recall: 1

overall: 0.706

📈 Turn Metrics

node_coverage: 0.500

edge_coverage: 0.171

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: 0.383

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 5

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

🤔 Assistant Thinking (click to toggle)
From the blue doorway I’m currently facing south (after two clockwise 90° turns total). To maximize coverage without moving, rotate 90° more to face west and observe; this should reveal objects either back into the original room or further along this room....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a consistent global (x,y) integer map using the initial origin (0,0) and cardinal directions; convert earlier relative/object-to-object constraints into absolute placements, and set the agent at the blue door with current facing west.
FINAL ANSWER: {
"agent": { "position": [4, -3], "f...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      4,
      -3
    ],
    "facing": "west"
  },
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "south"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      5,
      -1
    ]
  },
  "blue door": {
    "position": [
      4,
      -3
    ]
  },
  "shoppingcart": {
    "position": [
      4,
      -8
    ],
    "facing": "north"
  },
  "red chair": {
    "position": [
      2,
      -6
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      1,
      -5
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -1,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -7
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Observed)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map of only the currently visible objects, assigning integer coordinates consistent with their egocentric bearing/distance bins and converting their reported facings into local axes.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"bike": {
"positio...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

{
  "bike": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      1,
      4
    ],
    "facing": "west"
  },
  "desklamp": {
    "position": [
      -4,
      5
    ],
    "facing": "south"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

{
  "bike": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      1,
      4
    ],
    "facing": "west"
  },
  "desklamp": {
    "position": [
      -4,
      5
    ],
    "facing": "south"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.909

facing: 0.111

pos: 0.894

overall: 0.638

Global (Full):

dir: 0.500

facing: 0.083

pos: 0.656

overall: 0.413

Local:

dir: 1

facing: 1

pos: 0.867

overall: 0.956

Local (Newly):

dir: 1

facing: 1

pos: 0.867

overall: 0.956

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 0.714

edge_coverage: 0.244

is_action_fail: False

step: 5

action_counts:

move: 1

rotate: 3

return: 0

observe: 5

term: 0

forced_term: 0

query: 0

information_gain: 0.471

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 6

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• bike: front-left, mid distance, facing backward
• pan: front-slight-right, slightly far, facing forward
• desklamp: front-left, slightly far, facing left.
You have a maximum of 15 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on...

🤔 Assistant Thinking (click to toggle)
Continue a full scan from the blue doorway to avoid extra moves; rotate to face north and observe, which may reveal the remaining unseen objects/doors in either connected room....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single consistent global coordinate assignment (integer grid) that satisfies all observed egocentric bearings/distances and the reported near-object cardinal relations; place the agent at the blue door (current pose) and include only observed entities with facings where specified.
FIN...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "blue door": {
    "position": [
      4,
      -4
    ]
  },
  "yellow door": {
    "position": [
      5,
      -1
    ]
  },
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "south"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "south"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "north"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      1,
      -6
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      -2,
      -2
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -7
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Observed)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a locally consistent integer-coordinate map from current FOV, matching reported relative directions/proximities; use agent as origin with +y forward and +x right.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"shoes": { "position": [0, 3], "facing": "-x" },
"rubber...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shoes": {
      "position": [
        0,
        3
      ],
      "facing": "-x"
    },
    "rubberduck": {
      "position": [
        -1,
        3
      ],
      "facing": "+y"
    },
    "alarmclock": {
      "position": [
        -1,
        5
      ],
      "facing": "-x"
    },
    "yellow door": {
      "position": [
        2,
        3
      ]
    }
  }
}

🎯 Ground Truth

{
  "yellow door": {
    "position": [
      2,
      3
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      -2,
      3
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      -2,
      5
    ],
    "facing": "west"
  },
  "shoes": {
    "position": [
      0,
      3
    ],
    "facing": "west"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "shoes": {
      "position": [
        0,
        3
      ],
      "facing": "-x"
    },
    "rubberduck": {
      "position": [
        -1,
        3
      ],
      "facing": "+y"
    },
    "alarmclock": {
      "position": [
        -1,
        5
      ],
      "facing": "-x"
    },
    "yellow door": {
      "position": [
        2,
        3
      ]
    }
  }
}

🎯 Ground Truth

(no data)

🧠 Cognitive Map Metrics

Global:

dir: 0.909

facing: 0.111

pos: 0.894

overall: 0.638

Global (Full):

dir: 0.500

facing: 0.083

pos: 0.656

overall: 0.413

Local:

dir: 0.900

facing: 1

pos: 0.916

overall: 0.939

Local (Newly):

(none)

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 0.714

edge_coverage: 0.341

is_action_fail: False

step: 6

action_counts:

move: 1

rotate: 4

return: 0

observe: 6

term: 0

forced_term: 0

query: 0

information_gain: 0.614

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 7

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• shoes: front, mid distance, facing left
• rubberduck: front-left, mid distance, facing forward
• alarmclock: front-slight-left, slightly far, facing left
• yellow door: front-right, mid distance, on right wall
Assume your current facing direction is called ...

🤔 Assistant Thinking (click to toggle)
We’ve identified most objects but still haven’t seen laptop, airplane, backpack, or books. The yellow door is visible from here; jumping to that doorway should open visibility into its connected room and likely reveal remaining items....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a globally consistent approximate integer-coordinate map (origin at initial agent pose), using all observations and keeping object intrinsic facings consistent by converting egocentric facing to global cardinal directions.
FINAL ANSWER:
{
"agent": { "position": [7, -1], "facing": ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      7,
      -1
    ],
    "facing": "north"
  },
  "blue door": {
    "position": [
      5,
      -4
    ]
  },
  "yellow door": {
    "position": [
      7,
      -1
    ]
  },
  "shoes": {
    "position": [
      5,
      -1
    ],
    "facing": "west"
  },
  "rubberduck": {
    "position": [
      4,
      -1
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      4,
      1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      5,
      -10
    ],
    "facing": "south"
  },
  "red chair": {
    "position": [
      3,
      -7
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      2,
      -6
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "desklamp": {
    "position": [
      0,
      -7
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Observed)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      6,
      -1
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      6,
      -1
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: No objects or doors are currently in my field of view from the yellow door position, so the local cognitive map contains an empty objects list.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {}
}
```...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: A/F/E/C are in the upper-right area beyond the interior wall that hasn’t been visually covered; B is far to the right in the current open area and likely outside prior FOV coverage. D is adjacent to the agent and within the already traversed/observed vicinity.
FINAL ANSWER:
{"unexplored":["A...

🗺️ Symbolic Fog Map

################
#########...C..#
#########......#
##......#......#
##......#AF...E#
##......#......#
##......*D....B#
##......########
##......########
######+#########
#......#########
#......#########
#......#########
#......#########
#......#########
#......#########
################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, E, F

🎯 Ground Truth: E, B, D

🧠 Cognitive Map Metrics

Global:

dir: 0.879

facing: 1

pos: 0.853

overall: 0.911

Global (Full):

dir: 0.483

facing: 0.750

pos: 0.626

overall: 0.620

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 0.400

recall: 0.667

overall: 0.500

📈 Turn Metrics

node_coverage: 0.714

edge_coverage: 0.341

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: 0.614

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 8

👤 Environment Observation (click to toggle)
You jumped to yellow door. 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)
At the yellow doorway with no objects in current FOV; rotate to scan into the connected space. Try a 90° clockwise rotation and observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct an integer-coordinate global map consistent with all observed relative directions/distances and doorway jumps; infer each object's global facing from relative facing at time of observation.
FINAL ANSWER:
{
"agent": { "position": [6, -1], "facing": "east" },
"yellow door": { "po...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ]
  },
  "blue door": {
    "position": [
      4,
      -4
    ]
  },
  "shoes": {
    "position": [
      5,
      -1
    ],
    "facing": "west"
  },
  "rubberduck": {
    "position": [
      3,
      -1
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      3,
      0
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      1,
      -6
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -1,
      -1
    ],
    "facing": "west"
  },
  "desklamp": {
    "position": [
      -1,
      -7
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      9,
      0
    ]
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      11,
      1
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      11,
      2
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Observed)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centered) map for only the currently visible items, using the agent’s current facing as +y and assigning integer coordinates consistent with the reported relative bearings/distances and proximity relations.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"books": ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "books": {
      "position": [
        0,
        4
      ]
    },
    "laptop": {
      "position": [
        -2,
        4
      ],
      "facing": "+y"
    },
    "backpack": {
      "position": [
        -2,
        6
      ],
      "facing": "-x"
    },
    "airplane": {
      "position": [
        -4,
        6
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "airplane": {
    "position": [
      -4,
      6
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      -2,
      4
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      -2,
      6
    ],
    "facing": "north"
  },
  "books": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "books": {
      "position": [
        0,
        4
      ]
    },
    "laptop": {
      "position": [
        -2,
        4
      ],
      "facing": "+y"
    },
    "backpack": {
      "position": [
        -2,
        6
      ],
      "facing": "-x"
    },
    "airplane": {
      "position": [
        -4,
        6
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "airplane": {
    "position": [
      -4,
      6
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      -2,
      4
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      -2,
      6
    ],
    "facing": "north"
  },
  "books": {
    "position": [
      0,
      4
    ],
    "facing": "east"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.867

facing: 1

pos: 0.876

overall: 0.914

Global (Full):

dir: 0.867

facing: 1

pos: 0.876

overall: 0.914

Local:

dir: 1

facing: 1

pos: 1

overall: 1

Local (Newly):

dir: 1

facing: 1

pos: 1

overall: 1

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 1

edge_coverage: 0.439

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: 0.874

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 9

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• laptop: front-left, slightly far, facing forward
• airplane: front-left, slightly far, facing right
• backpack: front-slight-left, slightly far, facing left
• books: front, mid distance
Assume your current facing direction is called "north":
• laptop is sou...

🤔 Assistant Thinking (click to toggle)
All listed objects (12) and both doors have now been observed. Further scanning or querying would increase cost without improving coverage, so terminate exploration....

💬 Assistant Action
Actions: [Term()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single globally-consistent integer-coordinate map that satisfies all observed egocentric bearings, distance bins, and the reported near (≤2) proximity relations; door positions anchor cross-room alignment.
FINAL ANSWER: {
"agent": { "position": [6, -1], "facing": "east" },
"bl...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ]
  },
  "yellow door": {
    "position": [
      6,
      -1
    ]
  },
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "bike": {
    "position": [
      1,
      -6
    ],
    "facing": "east"
  },
  "pan": {
    "position": [
      -2,
      -3
    ],
    "facing": "west"
  },
  "desklamp": {
    "position": [
      -1,
      -7
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      9,
      -1
    ]
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      11,
      1
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      11,
      2
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Observed)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "shoes": {
    "position": [
      4,
      -1
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      4,
      -9
    ],
    "facing": "south"
  },
  "laptop": {
    "position": [
      10,
      1
    ],
    "facing": "east"
  },
  "rubberduck": {
    "position": [
      2,
      -1
    ],
    "facing": "north"
  },
  "bike": {
    "position": [
      2,
      -5
    ],
    "facing": "east"
  },
  "airplane": {
    "position": [
      12,
      3
    ],
    "facing": "south"
  },
  "pan": {
    "position": [
      0,
      -3
    ],
    "facing": "west"
  },
  "alarmclock": {
    "position": [
      2,
      1
    ],
    "facing": "west"
  },
  "red chair": {
    "position": [
      2,
      -7
    ],
    "facing": "south"
  },
  "backpack": {
    "position": [
      12,
      1
    ],
    "facing": "north"
  },
  "desklamp": {
    "position": [
      -1,
      -8
    ],
    "facing": "south"
  },
  "books": {
    "position": [
      10,
      -1
    ],
    "facing": "east"
  },
  "yellow door": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  },
  "blue door": {
    "position": [
      4,
      -4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      6,
      -1
    ],
    "facing": "east"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.958

facing: 1

pos: 0.899

overall: 0.953

Global (Full):

dir: 0.958

facing: 1

pos: 0.899

overall: 0.953

Local:

(none)

Local (Newly):

(none)

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 1

edge_coverage: 0.439

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: 0.874

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)
Start by checking the initial north-facing view; previously it had no objects. Any newly visible object would immediately indicate a position/orientation change affecting visibility....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single globally-consistent integer-coordinate map (origin at initial agent position) that satisfies all previously observed egocentric bearings, distance bins, and stated proximity relations; include facing where it was observed.
FINAL ANSWER:
```json
{
"agent": { "position": [...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.939

facing: 1

pos: 0.900

overall: 0.946

🧾 False Belief Object Sets

all_objects: ['airplane', 'alarmclock', 'backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'pan', 'red chair', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

all_changed_objects: ['airplane', 'alarmclock', 'pan', 'red chair']

newly_observed_changed (this turn): []

unchanged_objects: ['backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'alarmclock', 'backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'pan', 'red chair', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

🧭 False Belief Info

step: 1

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'red chair', 'ori': True}, {'name': 'pan', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'airplane', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 11

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

🤔 Assistant Thinking (click to toggle)
Previously, rotating 90° clockwise from start (facing east) revealed shoes, rubberduck, alarmclock, and both doors. Repeating that view is the fastest way to detect any changes among those items....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Need a consistent global integer-coordinate map including agent, all objects, and both doors, using the initial origin frame; since only binned distances/angles are available, place items at plausible coordinates consistent with observations and known relative relations.
FINAL ANSWER: {
"ag...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: airplane:

dir: None

pos: 0.780

facing: None

overall: None

Retention: airplane:

dir: None

pos: 0.380

facing: None

overall: None

Unchanged (all):

dir: 0.909

facing: 1

pos: 0.836

overall: 0.915

🧾 False Belief Object Sets

all_objects: ['airplane', 'alarmclock', 'backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'pan', 'red chair', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

all_changed_objects: ['airplane', 'alarmclock', 'pan', 'red chair']

newly_observed_changed (this turn): ['airplane']

unchanged_objects: ['backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

pred_changed_keys: ['airplane']

pred_unchanged_keys: ['airplane', 'alarmclock', 'backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'pan', 'red chair', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

🧭 False Belief Info

step: 2

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'red chair', 'ori': True}, {'name': 'pan', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'airplane', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['airplane']

newly_observed_unchanged_objects: ['yellow door', 'rubberduck', 'shoes', 'blue door']

🔄 FB Turn 12

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• shoes: front-slight-right, slightly far, facing backward
• rubberduck: front-right, mid distance, facing left
• airplane: front-right, slightly far, facing right
• yellow door: front-slight-right, slightly far, on front wall
• blue door: front-right, slight...

🤔 Assistant Thinking (click to toggle)
Airplane is now visible from the start-east view (it previously was only seen from the yellow-door room), and alarmclock is no longer visible here, so at least those two likely changed position. Next, re-check the blue-door side room objects from the blue doorway (high information gain, minimal move...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single consistent global map (integer grid) including agent, all objects, and both doors, using the initial position as origin and current agent pose at the last observed location (blue door).
FINAL ANSWER: {
"agent": { "position": [5, -5], "facing": "south" },
"yellow door": ...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: red chair:

dir: None

pos: None

facing: 1

overall: None

Retention: red chair:

dir: None

pos: None

facing: 0

overall: None

Changed: alarmclock:

dir: None

pos: 0.758

facing: None

overall: None

Retention: alarmclock:

dir: None

pos: 0.287

facing: None

overall: None

Unchanged (all):

dir: 0.621

facing: 1

pos: 0.674

overall: 0.765

🧾 False Belief Object Sets

all_objects: ['airplane', 'alarmclock', 'backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'pan', 'red chair', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

all_changed_objects: ['airplane', 'alarmclock', 'pan', 'red chair']

newly_observed_changed (this turn): ['alarmclock', 'red chair']

unchanged_objects: ['backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

pred_changed_keys: ['alarmclock', 'red chair']

pred_unchanged_keys: ['airplane', 'alarmclock', 'backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'pan', 'red chair', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

🧭 False Belief Info

step: 3

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'red chair', 'ori': True}, {'name': 'pan', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'airplane', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['red chair', 'alarmclock']

newly_observed_unchanged_objects: ['shoppingcart']

🔄 FB Turn 13

👤 Environment Observation (click to toggle)
You jumped to blue door. You rotated clockwise 90°. You observe:
• shoppingcart: front, slightly far, facing forward
• alarmclock: front-right, slightly far, facing right
• red chair: front-right, mid distance, facing right.
You have a maximum of 12 exploration steps left.
Strictly follow this form...

🤔 Assistant Thinking (click to toggle)
We’ve confirmed: airplane newly appears near the blue door side (position change) and alarmclock moved (now in this room/view). Red chair’s facing changed (orientation). Need one more changed object; next, scan the other side from the blue door (rotate 90° to match the earlier “bike/pan/desklamp” vi...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct an integer-coordinate global map consistent with all observations; facings converted to cardinal directions from the last known relative facings.
FINAL ANSWER:
{
"agent": { "position": [4, -4], "facing": "west" },
"shoes": { "position": [4, -1], "facing": "west" },
"shoppingca...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: pan:

dir: None

pos: None

facing: 1

overall: None

Retention: pan:

dir: None

pos: None

facing: 0

overall: None

Unchanged (all):

dir: 0.970

facing: 1

pos: 0.937

overall: 0.969

🧾 False Belief Object Sets

all_objects: ['airplane', 'alarmclock', 'backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'pan', 'red chair', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

all_changed_objects: ['airplane', 'alarmclock', 'pan', 'red chair']

newly_observed_changed (this turn): ['pan']

unchanged_objects: ['backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

pred_changed_keys: ['pan']

pred_unchanged_keys: ['airplane', 'alarmclock', 'backpack', 'bike', 'blue door', 'books', 'desklamp', 'laptop', 'pan', 'red chair', 'rubberduck', 'shoes', 'shoppingcart', 'yellow door']

🧭 False Belief Info

step: 4

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'red chair', 'ori': True}, {'name': 'pan', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'airplane', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['pan']

newly_observed_unchanged_objects: ['desklamp', 'bike']

🔄 FB Turn 14

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• bike: front-left, mid distance, facing backward
• airplane: front-right, near, facing left
• pan: front-slight-right, slightly far, facing backward
• desklamp: front-left, slightly far, facing left.
You have a maximum of 11 exploration steps left.
Strictly ...

🤔 Assistant Thinking (click to toggle)
Compare with the original map from the same viewpoints: airplane is now present near the blue door (was only in the yellow-door room before) → position change; alarmclock is now in the blue-door room view (previously in the start-east view) → position change; red chair’s facing differs at the blue-d...

💬 Assistant Action
Actions: [Term(changes="airplane: position, alarmclock: position, red chair: orientation, pan: orientation")]

📈 Turn Metrics

reward: 1

is_done: True

is_valid_action: True

success: 1

🧭 False Belief Info

step: 5

correctly_identified_changes: 1

f1_overall: 1

f1_position: 1

f1_facing: 1

action_cost: 4

action_cost_after_seen: 0

ground_truth_changes: [{'name': 'red chair', 'ori': True}, {'name': 'pan', 'ori': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'airplane', 'pos': True}]

reported_changes: [{'name': 'airplane', 'pos': True}, {'name': 'alarmclock', 'pos': True}, {'name': 'red chair', 'ori': True}, {'name': 'pan', 'ori': True}]

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

📊 Task: DirectionEvaluationTask

Question 1 (ID: fd2887aae65af114)

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

🤔 Assistant Answer (click to toggle)
THINK:
Backpack is north of laptop, while books are east of laptop, so backpack is northwest of books; their separation is √2, which is near.
FINAL ANSWER:
north-west, near...

✅ Evaluation Results

id: fd2887aae65af114

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

action: None

answer: north east, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Evaluation state — Q1: DirectionEvaluationTask

Question 2 (ID: 47e25a4138337ec9)

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

🤔 Assistant Answer (click to toggle)
THINK:
Infer relative positions via doorway observations: pan is far to the west (slightly north) of the blue door, while books are east of the yellow door; yellow door is northeast of the blue door. Net: books end up well to the east of pan at a far distance.
FINAL ANSWER:
east, far...

✅ Evaluation Results

id: 47e25a4138337ec9

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

action: None

answer: east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 05aacd3852716124)

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

🤔 Assistant Answer (click to toggle)
THINK:
Using the inferred global layout from door-relative observations, desklamp lies down and left from books by about 10m, which bins to south-west and far.
FINAL ANSWER:
south-west, far...

✅ Evaluation Results

id: 05aacd3852716124

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

action: None

answer: south west, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: PovEvaluationTask

Question 1 (ID: dd32b518994cc0cd)

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

🤔 Assistant Answer (click to toggle)
THINK:
From prior observations, alarmclock is near and northwest of rubberduck; if I jump to rubberduck and face its (northward) direction, that places alarmclock in my front-left at near distance.
FINAL ANSWER:
front-left, near...

✅ Evaluation Results

id: dd32b518994cc0cd

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

Question 2 (ID: b3bd1bdceefdf76a)

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

🤔 Assistant Answer (click to toggle)
THINK:
From the blue door view, shoppingcart was straight ahead and farther than the red chair (which was ahead-right and closer), so from the red chair the shoppingcart should be ahead and to the left at a moderate distance.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: b3bd1bdceefdf76a

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 9f4070bd1a76781d)

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

🤔 Assistant Answer (click to toggle)
THINK:
Backpack was observed to be north (in front) of the laptop at near distance; after jumping to the laptop and facing the laptop’s facing direction, backpack remains directly in front of me and near.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 9f4070bd1a76781d

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

action: None

answer: front, near

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: e0bba9ba020fcf43)

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

🤔 Assistant Answer (click to toggle)
THINK:
Alarmclock is near only to rubberduck, and rubberduck is east of alarmclock; if I’m at rubberduck facing its direction (left = west), alarmclock would appear in front.
FINAL ANSWER:
rubberduck...

✅ Evaluation Results

id: e0bba9ba020fcf43

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

action: None

answer:

answer: rubberduck

final_pos: [4, 10]

final_ori: [0, 1]

final_observation: [{'name': 'alarmclock', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}]

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 1

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

Question 2 (ID: 5ee18081cd74702f)

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

🤔 Assistant Answer (click to toggle)
THINK:
Backpack is near and directly in front, and airplane is front-left at mid distance. From earlier proximity info, laptop is near backpack (laptop is south of backpack), and airplane is west of backpack, which would place airplane northwest of laptop. If standing at the laptop and facing its di...

✅ Evaluation Results

id: 5ee18081cd74702f

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

action: None

answer:

answer: laptop

final_pos: [12, 12]

final_ori: [1, 0]

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

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 3

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

Question 3 (ID: 48ef54e966ae6b2b)

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

🤔 Assistant Answer (click to toggle)
THINK:
If I’m standing at an object and facing its direction, the blue door being front-left and on the left wall is consistent with being in room 2 facing east toward a door on the north wall. The object observed earlier that faces east and is positioned such that the blue door would lie northeast ...

✅ Evaluation Results

id: 48ef54e966ae6b2b

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

action: None

answer:

answer: bike

final_pos: [4, 6]

final_ori: [1, 0]

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

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 2

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 746bd0e0e6ba5a86)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
After the sequence, you end up at the yellow door facing west; shoes are 2 units directly ahead (west) of you, so they are in front at near distance.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 746bd0e0e6ba5a86

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

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: dd857cdb010f8d2c)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
After the sequence: rotate to face east, jump to the blue door (front-right, slightly far), rotate to face west, jump to the bike (front-left, mid). From the bike facing west, the desklamp lies further in the same front-left (southwest) direction, and it was not reported as a ≤2m proximity pa...

✅ Evaluation Results

id: dd857cdb010f8d2c

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

action: None

answer: front-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 3 (ID: f648d1cc302e836a)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
After the sequence, you are at the yellow door facing west. Shoes are 1 unit directly west of the yellow door, i.e., straight ahead and near.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: f648d1cc302e836a

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

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: a64c44dde1456896)

❓ 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-right, slightly far, facing right

What action sequence led to this final view? The action sequence must be v...

🤔 Assistant Answer (click to toggle)
THINK:
End at shoppingcart facing west; from there the bike would be front-right at slightly-far range. To legally JumpTo, first rotate to bring blue door into FOV, jump to it, rotate to see shoppingcart, jump there, then rotate to face west.
FINAL ANSWER:
Rotate(90), JumpTo(blue door), Rotate(90), ...

✅ Evaluation Results

id: a64c44dde1456896

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-right, slightly far, facing right What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [1, 3]

final_ori: [0, 1]

room_id: 2

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

minimal_plan: [['rotate', 90], ['jumpto', 'blue door'], ['rotate', 180], ['jumpto', 'desklamp'], ['rotate', 90]]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: False

final_pos: [6.0, 2.0]

final_ori: [-1, 0]

visible_match: False

score: 0

evaluation_mode: default

Question 2 (ID: c61a5d692c908db6)

❓ 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 right; blue door is at front-right, mid distance, on front wall

What action sequence led to th...

🤔 Assistant Answer (click to toggle)
THINK:
From the start, rotate to bring the blue door into view, jump to it, rotate to face into room 2 where the shoppingcart is visible, jump to the shoppingcart, then turn around to face the north wall where the blue door is located; from there the bike would appear ahead.
FINAL ANSWER:
Rotate(90)...

✅ Evaluation Results

id: c61a5d692c908db6

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 right; blue door is at front-right, 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: [4, 4]

final_ori: [0, 1]

room_id: 2

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'blue door'], ['jumpto', 'red chair'], ['rotate', 180]]

final_observation: [{'name': 'bike', 'direction': 'front', 'distance': 'near', 'orientation': 'facing right'}, {'name': 'blue door', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [6.0, 2.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: default

Question 3 (ID: 84f10bed2583426f)

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

🤔 Assistant Answer (click to toggle)
THINK:
From the start (facing north), rotate to face east so the yellow door is in view, jump to the yellow door, then rotate clockwise 90° to face south, matching the final view where airplane/backpack/laptop appear front-left.
FINAL ANSWER:
Rotate(90), JumpTo(yellow door), Rotate(90)...

✅ Evaluation Results

id: 84f10bed2583426f

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

action: None

answer:

final_pos: [8, 10]

final_ori: [1, 0]

room_id: [1, 3]

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

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

final_observation: [{'name': 'airplane', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'backpack', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'laptop', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: False

final_pos: [8.0, 10.0]

final_ori: [0, -1]

visible_match: False

score: 0

evaluation_mode: default

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: d07c09cead29a5ab)

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

🤔 Assistant Answer (click to toggle)
THINK:
Infer integer coordinates consistent with all observed relative bearings, distance bins, and proximity constraints across the two doorway vantage points (blue door and yellow door), anchored to the start at (0,0).
FINAL ANSWER:
(12, 0); (10, -2); (4, -9); (2, -1)...

✅ Evaluation Results

id: d07c09cead29a5ab

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

action: None

answer: [[12, 1], [10, -1], [4, -9], [2, -1]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.963

threshold: 0.900

score: 0.963

evaluation_mode: default

Question 2 (ID: c301d9dd74a52e57)

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

🤔 Assistant Answer (click to toggle)
THINK:
Infer integer coordinates consistent with all observed egocentric bearings, distance bins, and reported near-relations across the two doorway vantage points; then report allocentric (x,y) from the start as origin.
FINAL ANSWER:
(10, 1); (2, 1); (0, -7); (2, -1); (3, -6)...

✅ Evaluation Results

id: c301d9dd74a52e57

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

action: None

answer: [[10, 1], [2, 1], [-1, -8], [2, -1], [2, -7]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.871

threshold: 0.900

score: 0.871

evaluation_mode: default

Question 3 (ID: 4ade2017a10e41fb)

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

🤔 Assistant Answer (click to toggle)
THINK:
Infer a consistent integer-coordinate layout from the binned egocentric observations at each doorway and the given close-object cardinal relations (in the locally-defined “north” frames). Use jumps to doors as fixed reference points and choose coordinates satisfying all direction constraints ...

✅ Evaluation Results

id: 4ade2017a10e41fb

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

action: None

answer: [[10, -1], [4, -9], [10, 1], [2, 1], [2, -5]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.871

threshold: 0.900

score: 0.871

evaluation_mode: default

📊 Task: RotEvaluationTask

Question 1 (ID: d41a4cb1c4ae88e8)

❓ 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: desklamp, rubberduck, shoppingcart.
...

🤔 Assistant Answer (click to toggle)
THINK:
As I rotate counterclockwise from facing north, headings sweep from 0° down through negative bearings to -180°, then wrap to +180° and continue down to 0°. Desklamp lies at a negative (slightly west of south) bearing, while shoppingcart and rubberduck lie at positive (southeast) bearings, wit...

✅ Evaluation Results

id: d41a4cb1c4ae88e8

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: desklamp, rubberduck, shoppingcart. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['desklamp', 'shoppingcart', 'rubberduck']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: 35aed4a89a7998dd)

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

🤔 Assistant Answer (click to toggle)
THINK:
Determine each object’s approximate bearing from the start (facing north), then sort by clockwise bearing: backpack ≈ 80°, rubberduck ≈ 120°, pan ≈ 280°.
FINAL ANSWER:
backpack, rubberduck, pan...

✅ Evaluation Results

id: 35aed4a89a7998dd

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 070834d55928c793)

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

🤔 Assistant Answer (click to toggle)
THINK:
Determine each object’s approximate absolute bearing from the start (facing north), then sort by increasing clockwise bearing (0→360). Bearings place backpack closest to east (~90°), rubberduck southeast (~120°), shoppingcart farther toward south (~160°).
FINAL ANSWER:
backpack, rubberduck, s...

✅ Evaluation Results

id: 070834d55928c793

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

action: None

answer: ['backpack', 'rubberduck', 'shoppingcart']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: RotDualEvaluationTask

Question 1 (ID: 51578be02150882b)

❓ 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:
desklamp, shoppingcart, rubb...

🤔 Assistant Answer (click to toggle)
THINK:
The observed front-alignments occur in decreasing bearing order from the start (near 180° down toward 0°), which matches a counterclockwise 360° sweep (0 → -180 → +180 → 0).
FINAL ANSWER:
counterclockwise...

✅ Evaluation Results

id: 51578be02150882b

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: desklamp, shoppingcart, rubberduck 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: a232cb4d3c853647)

❓ 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:
backpack, rubberduck, pan

B...

🤔 Assistant Answer (click to toggle)
THINK:
Rubberduck is at a more southerly/easterly bearing than pan, and the given front-facing sequence (backpack → rubberduck → pan) matches a sweep of headings from larger bearings down to smaller ones, which happens when rotating counterclockwise.
FINAL ANSWER:
counterclockwise...

✅ Evaluation Results

id: a232cb4d3c853647

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: backpack, rubberduck, pan 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: 83fd00114d015923)

❓ 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:
backpack, rubberduck, shoppi...

🤔 Assistant Answer (click to toggle)
THINK:
The sequence backpack → rubberduck → shoppingcart matches encountering objects at increasing azimuth from the initial north-facing direction, which happens when rotating clockwise.
FINAL ANSWER:
clockwise...

✅ Evaluation Results

id: 83fd00114d015923

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: backpack, rubberduck, shoppingcart Based on this sequence, in which direction did you rotate? Answer format: clockwise or counterclockwise Example: clockwise

action: None

answer: clockwise

task_type: RotDualEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 12e5714b691ffd9d)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (4, -2) and face north.
What is the egocentric relation of shoes?

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

🤔 Assistant Answer (click to toggle)
THINK:
From the updated position (4, -2) facing north, shoes are one step to the east and one step to the north, giving a 45° bearing (front-right) and a near distance.
FINAL ANSWER:
front-right, near...

✅ Evaluation Results

id: 12e5714b691ffd9d

question: Still treat your initial position as origin (0, 0) You move to (4, -2) and face north. What is the egocentric relation of shoes? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 2 (ID: 37203b723cb2ed85)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (4, 2) and face south.
What is the egocentric relation of rubberduck?

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

🤔 Assistant Answer (click to toggle)
THINK:
From (4, 2) facing south, rubberduck lies mostly ahead and a bit to the left at a distance just over 4, so it is front-slight-left and slightly far.
FINAL ANSWER:
front-slight-left, slightly far...

✅ Evaluation Results

id: 37203b723cb2ed85

question: Still treat your initial position as origin (0, 0) You move to (4, 2) and face south. What is the egocentric relation of rubberduck? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Question 3 (ID: 1e0d4b92e81bdd63)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (5, 0) and face west.
What is the egocentric relation of pan?

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

🤔 Assistant Answer (click to toggle)
THINK:
Facing west, pan is ahead and slightly to the left of the agent; the separation falls in the slightly-far distance bin.
FINAL ANSWER:
front-slight-left, slightly far...

✅ Evaluation Results

id: 1e0d4b92e81bdd63

question: Still treat your initial position as origin (0, 0) You move to (5, 0) and face west. What is the egocentric relation of pan? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-left, slightly far

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 7eb7d98b38d92500)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: shoes is at front, near, facing left; alarmclock is at front-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)
...

🤔 Assistant Answer (click to toggle)
THINK:
From earlier mapping consistent with all observations: shoes at (4, -1) and alarmclock at (2, 1). New pose faces north; shoes is directly in front at near distance, so agent is 1 step south of shoes at (4, -2). From (4, -2) to alarmclock is (-2, +3), which is front-left at mid distance, match...

✅ Evaluation Results

id: 7eb7d98b38d92500

question: You move to a new location and face north. You observe: shoes is at front, near, facing left; alarmclock is at front-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: [4, -2]

final_pos: [6, 9]

final_ori: [0, 1]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'shoes', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'alarmclock', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: default

Question 2 (ID: 4190e9e80fa5f994)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: rubberduck is at front-right, mid distance, facing backward; blue door is at front, slightly far, on front wall; pan is at front-right, slightly far, facing right
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate...

🤔 Assistant Answer (click to toggle)
THINK:
Blue door is directly in front while I face south, so I share its x-coordinate and am 4–8 units north of it. Testing integer candidates against rubberduck (front-right, mid) and pan (front-right, slightly far) yields a consistent position at (4, 1).
FINAL ANSWER:
(4, 1)...

✅ Evaluation Results

id: 4190e9e80fa5f994

question: You move to a new location and face south. You observe: rubberduck is at front-right, mid distance, facing backward; blue door is at front, slightly far, on front wall; pan is at front-right, slightly far, 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: [4, 2]

final_pos: [6, 13]

final_ori: [0, -1]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

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

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.800

raw_score: 0.800

score: 0.800

evaluation_mode: default

Question 3 (ID: e006710c90936706)

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

🤔 Assistant Answer (click to toggle)
THINK:
Use earlier inferred object coordinates consistent with all observations: alarmclock at (2, 1) and rubberduck at (2, -1). New pose faces west, so both being front-slight-right/left implies agent is east of them with y between them, giving y=0. Alarmclock being front-slight-right (small upward...

✅ Evaluation Results

id: e006710c90936706

question: You move to a new location and face west. You observe: pan is at front-left, slightly far, facing forward; alarmclock is at front-slight-right, mid distance, facing forward; rubberduck is at front-slight-left, 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: [5, 0]

final_pos: [7, 11]

final_ori: [-1, 0]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'pan', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'alarmclock', 'direction': 'front-slight-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'rubberduck', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing right'}]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: default

📊 Task: DirectionEvaluationTask

Question 1 (ID: fd2887aae65af114)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent global coordinate map (integer grid) from the qualitative observations, anchoring the start at (0,0). Then compute backpack’s direction and distance relative to books from the top-down map.
FINAL ANSWER:
<cogmap>
```json
{
"agent": { "position": [6, -1], "facin...

✅ Evaluation Results

id: fd2887aae65af114

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

action: None

answer: north east, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 47e25a4138337ec9)

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

🤔 Assistant Answer (click to toggle)
THINK:
I need to output an integer-coordinate global cognitive map (origin at the agent’s initial position, +y = north) containing only observed objects/doors, adding a cardinal facing for objects that had an observed facing. Exact coordinates were never queried, so I’ll place objects at consistent ...

✅ Evaluation Results

id: 47e25a4138337ec9

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

action: None

answer: east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 05aacd3852716124)

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

🤔 Assistant Answer (click to toggle)
THINK:
I will build a single global (top-down) coordinate map consistent with the sequence of egocentric observations, anchoring the start at (0,0) facing north, and placing doors/objects at integer coordinates that satisfy the reported relative bearings/distances and proximity constraints. Then I’l...

✅ Evaluation Results

id: 05aacd3852716124

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

action: None

answer: south west, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: dd32b518994cc0cd)

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

🤔 Assistant Answer (click to toggle)
THINK:
I need integer coordinates in the global frame where the initial agent position is (0,0) and initial facing is north. Since no Query() actions were used, I assign a single consistent set of integer coordinates that satisfy all observed egocentric bearings, distance bins, and the reported near...

✅ Evaluation Results

id: dd32b518994cc0cd

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

Question 2 (ID: b3bd1bdceefdf76a)

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

🤔 Assistant Answer (click to toggle)
THINK:
I must output a global cognitive map (integer coordinates) using the initial agent pose as origin. Since no Query() actions were used, coordinates are inferred to be a consistent approximate layout from the relative observations at the two doorways.
For the question: when jumping to the red...

✅ Evaluation Results

id: b3bd1bdceefdf76a

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 9f4070bd1a76781d)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll construct a single global integer-coordinate map consistent with all relative observations made from the origin, the blue door position, and the yellow door position, and convert each object’s reported “facing” into global cardinal directions based on the agent’s orientation at the time ...

✅ Evaluation Results

id: 9f4070bd1a76781d

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

action: None

answer: front, near

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: e0bba9ba020fcf43)

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

🤔 Assistant Answer (click to toggle)
THINK:
I must output integer coordinates in a single global frame (origin at initial agent position). Since no Query() was used, I construct one internally consistent layout that matches the observed distance/angle bins and the provided near-relations, then convert each object’s observed “facing” (f...

✅ Evaluation Results

id: e0bba9ba020fcf43

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

action: None

answer:

answer: rubberduck

final_pos: [4, 10]

final_ori: [0, 1]

final_observation: [{'name': 'alarmclock', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}]

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 1

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 5ee18081cd74702f)

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

🤔 Assistant Answer (click to toggle)
THINK:
I need to output a single consistent global (x,y) integer map using the initial agent pose as origin, and convert each object’s observed facing (forward/backward/left/right relative to my view at the time) into a global cardinal facing. Then answer the localization question by checking which ...

✅ Evaluation Results

id: 5ee18081cd74702f

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

action: None

answer:

answer: laptop

final_pos: [12, 12]

final_ori: [1, 0]

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

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 3

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 48ef54e966ae6b2b)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single global coordinate assignment consistent with all observed relative directions/distances and object facings; then test each object (standing at it, facing its direction) to see from where the blue door would appear front-left at mid distance. The bike best matches this obser...

✅ Evaluation Results

id: 48ef54e966ae6b2b

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

action: None

answer:

answer: bike

final_pos: [4, 6]

final_ori: [1, 0]

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

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 2

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: dd857cdb010f8d2c)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
I must output a global cognitive map with integer coordinates (origin at the agent’s initial [0,0], initial facing north). Since no Query() actions were used, I construct a single consistent coordinate assignment that matches the observed jump targets and relative bearings well enough to answ...

✅ Evaluation Results

id: dd857cdb010f8d2c

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

action: None

answer: front-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 2 (ID: f648d1cc302e836a)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single global coordinate map consistent with all egocentric observations and the provided proximity constraints, then simulate the given action sequence from the start pose to determine the final relative position of shoes.
FINAL ANSWER:
<cogmap>
```json
{
"agent": { "position":...

✅ Evaluation Results

id: f648d1cc302e836a

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

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 746bd0e0e6ba5a86)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
I assign integer coordinates consistent with the observed egocentric bins and the given near-relations (interpreting the “Assume your current facing direction is called north” frame as relative to the agent’s facing at that moment). Then I simulate the action sequence from the start to determ...

✅ Evaluation Results

id: 746bd0e0e6ba5a86

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

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: a64c44dde1456896)

❓ 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-right, slightly far, facing right

What action sequence led to this final view? The action sequence must be v...

🤔 Assistant Answer (click to toggle)
THINK:
I will provide a single consistent (integer) global coordinate assignment for all observed items and doors relative to the start (0,0), and then give a valid move-only sequence that places the agent at the blue doorway facing south so the bike appears front-right.
FINAL ANSWER:
<cogmap>
```...

✅ Evaluation Results

id: a64c44dde1456896

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-right, slightly far, facing right What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [1, 3]

final_ori: [0, 1]

room_id: 2

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

minimal_plan: [['rotate', 90], ['jumpto', 'blue door'], ['rotate', 180], ['jumpto', 'desklamp'], ['rotate', 90]]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: False

final_pos: [6.0, 7.0]

final_ori: [0, -1]

visible_match: False

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: c61a5d692c908db6)

❓ 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 right; blue door is at front-right, mid distance, on front wall

What action sequence led to th...

🤔 Assistant Answer (click to toggle)
THINK:
Cognitive map: assign a consistent integer-coordinate layout that matches the observed connectivity (room1 linked to room2 via blue door; room1 linked to room3 via yellow door) and the relative placements seen from the two doorways. Convert each object’s egocentric “facing” into global cardin...

✅ Evaluation Results

id: c61a5d692c908db6

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 right; blue door is at front-right, 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: [4, 4]

final_ori: [0, 1]

room_id: 2

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'blue door'], ['jumpto', 'red chair'], ['rotate', 180]]

final_observation: [{'name': 'bike', 'direction': 'front', 'distance': 'near', 'orientation': 'facing right'}, {'name': 'blue door', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [6.0, 2.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: prompt_cogmap

Question 3 (ID: 84f10bed2583426f)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll provide a single consistent integer-coordinate global map using the initial position as origin and converting observed relative bearings/facings into a plausible layout. Then I’ll match the described final view to the minimal move-only sequence that places the agent at the yellow door fa...

✅ Evaluation Results

id: 84f10bed2583426f

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

action: None

answer:

final_pos: [8, 10]

final_ori: [1, 0]

room_id: [1, 3]

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

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

final_observation: [{'name': 'airplane', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'backpack', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'laptop', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [8.0, 10.0]

final_ori: [1, 0]

visible_match: True

score: 1

evaluation_mode: prompt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: d07c09cead29a5ab)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent global grid using the initial pose as origin, placing doors and objects at integer coordinates that satisfy the observed relative bearings/distances and key proximity constraints as closely as possible. Then read off requested object coordinates.
FINAL ANSWER:
<c...

✅ Evaluation Results

id: d07c09cead29a5ab

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

action: None

answer: [[12, 1], [10, -1], [4, -9], [2, -1]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.876

threshold: 0.900

score: 0.876

evaluation_mode: prompt_cogmap

Question 2 (ID: c301d9dd74a52e57)

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

🤔 Assistant Answer (click to toggle)
THINK:
I don’t have any Query() outputs (exact coordinates), so I’m constructing a single internally-consistent global integer grid using the relative observations (bearings + binned distances) across the visited door viewpoints. Object “facing” is converted approximately into global cardinal direct...

✅ Evaluation Results

id: c301d9dd74a52e57

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

action: None

answer: [[10, 1], [2, 1], [-1, -8], [2, -1], [2, -7]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.646

threshold: 0.900

score: 0.646

evaluation_mode: prompt_cogmap

Question 3 (ID: 4ade2017a10e41fb)

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

🤔 Assistant Answer (click to toggle)
THINK:
I must output a single consistent global (allocentric) integer-coordinate map using the start as (0,0), +y as north, +x as east. Since no Query() actions were used, I construct the simplest integer layout that satisfies the observed egocentric bearings, distance bins, doorway jumps, and repor...

✅ Evaluation Results

id: 4ade2017a10e41fb

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

action: None

answer: [[10, -1], [4, -9], [10, 1], [2, 1], [2, -5]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.963

threshold: 0.900

score: 0.963

evaluation_mode: prompt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: d41a4cb1c4ae88e8)

❓ 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: desklamp, rubberduck, shoppingcart.
...

🤔 Assistant Answer (click to toggle)
THINK:
I must output a global JSON cognitive map with integer coordinates (origin at the agent’s initial position, +y = north). Since no Query() actions were used, exact coordinates were not directly given; I will provide a single consistent integer layout that matches all observed relative bearings...

✅ Evaluation Results

id: d41a4cb1c4ae88e8

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: desklamp, rubberduck, shoppingcart. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['desklamp', 'shoppingcart', 'rubberduck']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 35aed4a89a7998dd)

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

🤔 Assistant Answer (click to toggle)
THINK:
I must output a global JSON cognitive map using the initial agent position as origin and cardinal facings. Since no Query() actions were used, exact coordinates are not uniquely determined from binned bearings/distances; I will provide a single consistent integer-coordinate layout that preser...

✅ Evaluation Results

id: 35aed4a89a7998dd

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 070834d55928c793)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll construct a single global (origin-at-start) integer-coordinate map consistent with the observed relative bearings/distances and the given near-relations. Then, for the rotation question, I’ll compute each target object’s clockwise bearing from start (0,0) when facing north and sort by in...

✅ Evaluation Results

id: 070834d55928c793

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

action: None

answer: ['backpack', 'rubberduck', 'shoppingcart']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 12e5714b691ffd9d)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (4, -2) and face north.
What is the egocentric relation of shoes?

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

🤔 Assistant Answer (click to toggle)
THINK:
I need an integer-coordinate global cognitive map anchored at the initial agent pose (0,0) facing north. Since I never used Query(), I reconstruct a consistent approximate integer layout that matches all my binned egocentric observations (especially keeping shoes consistent).
For the questi...

✅ Evaluation Results

id: 12e5714b691ffd9d

question: Still treat your initial position as origin (0, 0) You move to (4, -2) and face north. What is the egocentric relation of shoes? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 37203b723cb2ed85)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (4, 2) and face south.
What is the egocentric relation of rubberduck?

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

🤔 Assistant Answer (click to toggle)
THINK:
I need to output a single global (origin-at-start) integer-coordinate JSON map containing the agent, all observed objects, and both doors. Since only binned distances/bearings were available (no Query), I assign a consistent integer layout that matches all observations and “near” (≤2) proximi...

✅ Evaluation Results

id: 37203b723cb2ed85

question: Still treat your initial position as origin (0, 0) You move to (4, 2) and face south. What is the egocentric relation of rubberduck? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 3 (ID: 1e0d4b92e81bdd63)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (5, 0) and face west.
What is the egocentric relation of pan?

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

🤔 Assistant Answer (click to toggle)
THINK:
Build a single global (x,y) map consistent with all my observations by assigning integer coordinates relative to the start (0,0), then compute the pan’s egocentric direction/distance from the new posed location (5,0) facing west.
FINAL ANSWER:
<cogmap>
{
"agent": { "position": [7, -1], "fac...

✅ Evaluation Results

id: 1e0d4b92e81bdd63

question: Still treat your initial position as origin (0, 0) You move to (5, 0) and face west. What is the egocentric relation of pan? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-left, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 7eb7d98b38d92500)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: shoes is at front, near, facing left; alarmclock is at front-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)
...

🤔 Assistant Answer (click to toggle)
THINK:
Derive a consistent integer-coordinate layout that satisfies the observed egocentric bearings/distances and the given near-relations. Use initial agent position as (0,0), +y=north, +x=east. Use the final agent pose at termination (at yellow door, facing east).
For the query: with the new ob...

✅ Evaluation Results

id: 7eb7d98b38d92500

question: You move to a new location and face north. You observe: shoes is at front, near, facing left; alarmclock is at front-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: [4, -2]

final_pos: [6, 9]

final_ori: [0, 1]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'shoes', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'alarmclock', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 4190e9e80fa5f994)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: rubberduck is at front-right, mid distance, facing backward; blue door is at front, slightly far, on front wall; pan is at front-right, slightly far, facing right
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate...

🤔 Assistant Answer (click to toggle)
THINK:
I constructed a single consistent global integer-coordinate map (origin at the initial agent position) that satisfies the observed relative bearings/distances and the stated near-relations, then solved the new-position question by finding the unique point where blue door is directly ahead (fa...

✅ Evaluation Results

id: 4190e9e80fa5f994

question: You move to a new location and face south. You observe: rubberduck is at front-right, mid distance, facing backward; blue door is at front, slightly far, on front wall; pan is at front-right, slightly far, 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: [4, 2]

final_pos: [6, 13]

final_ori: [0, -1]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

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

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.800

raw_score: 0.800

score: 0.800

evaluation_mode: prompt_cogmap

Question 3 (ID: e006710c90936706)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single global integer-coordinate map consistent with all prior relative observations (doors as anchors), convert each object’s observed facing (relative-to-agent at observation time) into global cardinal facing, then solve the final pose by finding the unique integer agent positio...

✅ Evaluation Results

id: e006710c90936706

question: You move to a new location and face west. You observe: pan is at front-left, slightly far, facing forward; alarmclock is at front-slight-right, mid distance, facing forward; rubberduck is at front-slight-left, 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: [5, 0]

final_pos: [7, 11]

final_ori: [-1, 0]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'pan', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'alarmclock', 'direction': 'front-slight-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'rubberduck', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing right'}]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: prompt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: fd2887aae65af114)

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

🤔 Assistant Answer (click to toggle)
THINK:
Backpack (12,1) is offset from books (10,-1) by (+2,+2): north east; distance √8≈2.83 → mid distance.
FINAL ANSWER:
north east, mid distance...

✅ Evaluation Results

id: fd2887aae65af114

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

action: None

answer: north east, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 47e25a4138337ec9)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from pan (0,-3) to books (10,-1): +10 east, +2 north → bearing in the east bin; distance √(10²+2²) ≈ 10.2 → far.
FINAL ANSWER:
east, far...

✅ Evaluation Results

id: 47e25a4138337ec9

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

action: None

answer: east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 05aacd3852716124)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from books (10,-1) to desklamp (-1,-8): (-11,-7) → south-west; distance √170 ≈ 13.0 → far.
FINAL ANSWER:
south-west, far...

✅ Evaluation Results

id: 05aacd3852716124

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

action: None

answer: south west, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: dd32b518994cc0cd)

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

🤔 Assistant Answer (click to toggle)
THINK:
After jumping, I am at rubberduck (2,-1) facing north. Alarmclock at (2,1) is directly ahead by 2 units, which is near.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: dd32b518994cc0cd

question: Now you jump to rubberduck's direction, facing its direction. Describe where alarmclock 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

Question 2 (ID: b3bd1bdceefdf76a)

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

🤔 Assistant Answer (click to toggle)
THINK:
From red chair at (2,-7) facing south, shoppingcart at (4,-9) is 2 forward and 2 left; distance √8≈2.83 → mid distance.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: b3bd1bdceefdf76a

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 9f4070bd1a76781d)

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

🤔 Assistant Answer (click to toggle)
THINK:
At laptop (10,1) facing east; backpack at (12,1) is 2 units straight ahead.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 9f4070bd1a76781d

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

action: None

answer: front, near

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: e0bba9ba020fcf43)

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

🤔 Assistant Answer (click to toggle)
THINK:
Alarmclock at (2,1) is 2 units north of rubberduck at (2,-1). If standing at rubberduck and facing its direction (north), alarmclock would be directly in front and near; alarmclock faces west which is left relative to facing north.
FINAL ANSWER:
rubberduck...

✅ Evaluation Results

id: e0bba9ba020fcf43

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

action: None

answer:

answer: rubberduck

final_pos: [4, 10]

final_ori: [0, 1]

final_observation: [{'name': 'alarmclock', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}]

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 1

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 5ee18081cd74702f)

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

🤔 Assistant Answer (click to toggle)
THINK:
Check which object’s position/facing makes backpack 2m straight ahead and oriented left relative to me, and airplane ahead-left at ~2.8m oriented right; this matches standing at the laptop (10,1) facing east.
FINAL ANSWER:
laptop...

✅ Evaluation Results

id: 5ee18081cd74702f

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

action: None

answer:

answer: laptop

final_pos: [12, 12]

final_ori: [1, 0]

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

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 3

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 48ef54e966ae6b2b)

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

🤔 Assistant Answer (click to toggle)
THINK:
Check which object’s position and facing direction makes the blue door appear front-left at a mid distance. From the bike at (2,-5) facing east, the blue door at (4,-4) is ahead-left with distance √5≈2.24 (mid), matching the observation.
FINAL ANSWER:
bike...

✅ Evaluation Results

id: 48ef54e966ae6b2b

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

action: None

answer:

answer: bike

final_pos: [4, 6]

final_ori: [1, 0]

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

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 2

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: dd857cdb010f8d2c)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute final pose after rotations/jumps, then transform vector from agent to desklamp into agent frame (facing west) to bin egocentric direction and distance.
FINAL ANSWER:
front-left, slightly far...

✅ Evaluation Results

id: dd857cdb010f8d2c

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

action: None

answer: front-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: f648d1cc302e836a)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute final pose: start (0,0) face north → Rotate(90) face east. Front-right mid distance object is rubberduck at (2,-1) → jump there. From (2,-1) facing east, front mid distance object is yellow door at (6,-1) → jump there. Rotate(-180) → face west. Shoes at (4,-1) is 2 units directly ahea...

✅ Evaluation Results

id: f648d1cc302e836a

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

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 746bd0e0e6ba5a86)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
After the moves, I’m at (6,-1) facing west. Shoes are at (4,-1), which is 2 units straight ahead (west), so they are in front at near distance.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 746bd0e0e6ba5a86

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

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: a64c44dde1456896)

❓ 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-right, slightly far, facing right

What action sequence led to this final view? The action sequence must be v...

🤔 Assistant Answer (click to toggle)
THINK:
To see the bike at front-right and slightly far, stand at desklamp (-1,-8) facing north: bike vector is (3,3), ~45° and distance ~4.24. Since first action can’t be JumpTo, start with a no-op rotation.
FINAL ANSWER:
Rotate(0), JumpTo(desklamp)...

✅ Evaluation Results

id: a64c44dde1456896

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-right, slightly far, facing right What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [1, 3]

final_ori: [0, 1]

room_id: 2

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

minimal_plan: [['rotate', 90], ['jumpto', 'blue door'], ['rotate', 180], ['jumpto', 'desklamp'], ['rotate', 90]]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

error: target_not_visible

score: 0

evaluation_mode: use_gt_cogmap

Question 2 (ID: c61a5d692c908db6)

❓ 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 right; blue door is at front-right, mid distance, on front wall

What action sequence led to th...

🤔 Assistant Answer (click to toggle)
THINK:
To end seeing the bike directly ahead at near distance while also seeing the blue door front-right, the agent should be at the red chair’s position (2,-7) facing north. From the start (0,0) facing north, rotate to face south to bring the red chair into FOV, jump to it, then rotate back to nor...

✅ Evaluation Results

id: c61a5d692c908db6

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 right; blue door is at front-right, 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: [4, 4]

final_ori: [0, 1]

room_id: 2

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'blue door'], ['jumpto', 'red chair'], ['rotate', 180]]

final_observation: [{'name': 'bike', 'direction': 'front', 'distance': 'near', 'orientation': 'facing right'}, {'name': 'blue door', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [4.0, 4.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 84f10bed2583426f)

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

🤔 Assistant Answer (click to toggle)
THINK:
We need to end at the yellow door (6,-1) facing east so airplane/laptop appear front-left and backpack front-slight-left at slightly-far distances. From start facing north, rotate 90° to face east, then jump to the yellow door.
FINAL ANSWER:
Rotate(90), JumpTo(yellow door)...

✅ Evaluation Results

id: 84f10bed2583426f

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

action: None

answer:

final_pos: [8, 10]

final_ori: [1, 0]

room_id: [1, 3]

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

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

final_observation: [{'name': 'airplane', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'backpack', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'laptop', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [8.0, 10.0]

final_ori: [1, 0]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: d07c09cead29a5ab)

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

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided reference cognitive map coordinates and report them in the requested object order.
FINAL ANSWER:
(12, 1); (10, -1); (4, -9); (2, -1)...

✅ Evaluation Results

id: d07c09cead29a5ab

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

action: None

answer: [[12, 1], [10, -1], [4, -9], [2, -1]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: c301d9dd74a52e57)

❓ 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: laptop, alarmclock, desklamp, rubberduck, red chair.
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 (agent start is origin) and report the requested objects in the specified order.
FINAL ANSWER:
(10, 1); (2, 1); (-1, -8); (2, -1); (2, -7)...

✅ Evaluation Results

id: c301d9dd74a52e57

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

action: None

answer: [[10, 1], [2, 1], [-1, -8], [2, -1], [2, -7]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 4ade2017a10e41fb)

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

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided reference cognitive map (allocentric, origin at agent start) and output coordinates in the requested object order.
FINAL ANSWER:
(10, -1); (4, -9); (10, 1); (2, 1); (2, -5)...

✅ Evaluation Results

id: 4ade2017a10e41fb

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

action: None

answer: [[10, -1], [4, -9], [10, 1], [2, 1], [2, -5]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: d41a4cb1c4ae88e8)

❓ 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: desklamp, rubberduck, shoppingcart.
...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s bearing from the agent at (0,0) with 0° = north and clockwise positive. During a continuous counterclockwise rotation, headings decrease from 0° down to -180°, then wrap to +180° and continue decreasing. Order is by when each bearing is reached.
FINAL ANSWER:
desklamp, s...

✅ Evaluation Results

id: d41a4cb1c4ae88e8

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: desklamp, rubberduck, shoppingcart. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['desklamp', 'shoppingcart', 'rubberduck']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 35aed4a89a7998dd)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute clockwise bearings from facing north (0°) using atan2(x,y): backpack ≈85°, rubberduck ≈117°, pan =180°. Sort by increasing bearing for the rotation order.
FINAL ANSWER:
backpack, rubberduck, pan...

✅ Evaluation Results

id: 35aed4a89a7998dd

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 070834d55928c793)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s clockwise bearing from agent facing north (0°): backpack ≈85°, rubberduck ≈117°, shoppingcart ≈156°. Order by increasing bearing during clockwise rotation.
FINAL ANSWER:
backpack, rubberduck, shoppingcart...

✅ Evaluation Results

id: 070834d55928c793

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

action: None

answer: ['backpack', 'rubberduck', 'shoppingcart']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 12e5714b691ffd9d)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (4, -2) and face north.
What is the egocentric relation of shoes?

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (4,-2) facing north; shoes at (4,-1) is 1 unit directly ahead, so front and near.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 12e5714b691ffd9d

question: Still treat your initial position as origin (0, 0) You move to (4, -2) and face north. What is the egocentric relation of shoes? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 37203b723cb2ed85)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (4, 2) and face south.
What is the egocentric relation of rubberduck?

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (4,2) facing south; rubberduck at (2,-1) gives relative vector (-2,-3). In agent frame (facing south), forward component = 3 and right component = 2 → front-right. Distance sqrt(13) ≈ 3.6 → mid distance.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 37203b723cb2ed85

question: Still treat your initial position as origin (0, 0) You move to (4, 2) and face south. What is the egocentric relation of rubberduck? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 1e0d4b92e81bdd63)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (5, 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 (5,0) facing west, pan at (0,-3) is 5 units ahead (west) and 3 units to the left (south). Angle ≈ -31° → front-left. Distance √34 ≈ 5.8 → slightly far.
FINAL ANSWER:
front-left, slightly far...

✅ Evaluation Results

id: 1e0d4b92e81bdd63

question: Still treat your initial position as origin (0, 0) You move to (5, 0) and face west. What is the egocentric relation of pan? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-left, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 7eb7d98b38d92500)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: shoes is at front, near, facing left; alarmclock is at front-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)
...

🤔 Assistant Answer (click to toggle)
THINK:
Shoes at (4, -1) is directly in front and near, so I must be on the same x=4 and 1–2 units south: y∈{-2,-3}. Alarmclock at (2, 1) should then be front-left at mid distance; from (4, -2) the vector is (-2, 3) giving distance √13≈3.6 (mid) and bearing ≈-33.7° (front-left). From (4, -3) distance...

✅ Evaluation Results

id: 7eb7d98b38d92500

question: You move to a new location and face north. You observe: shoes is at front, near, facing left; alarmclock is at front-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: [4, -2]

final_pos: [6, 9]

final_ori: [0, 1]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'shoes', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'alarmclock', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 4190e9e80fa5f994)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: rubberduck is at front-right, mid distance, facing backward; blue door is at front, slightly far, on front wall; pan is at front-right, slightly far, facing right
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate...

🤔 Assistant Answer (click to toggle)
THINK:
Facing south, “blue door at front” implies the agent is directly north of the blue door with the same x-coordinate. Blue door is at (4,-4), so agent x=4 and y>-4. Using “rubberduck front-right, mid distance” and “pan front-right, slightly far” fits best at (4,1) (rubberduck at distance √8≈2.8...

✅ Evaluation Results

id: 4190e9e80fa5f994

question: You move to a new location and face south. You observe: rubberduck is at front-right, mid distance, facing backward; blue door is at front, slightly far, on front wall; pan is at front-right, slightly far, 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: [4, 2]

final_pos: [6, 13]

final_ori: [0, -1]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

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

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.800

raw_score: 0.800

score: 0.800

evaluation_mode: use_gt_cogmap

Question 3 (ID: e006710c90936706)

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

🤔 Assistant Answer (click to toggle)
THINK:
Facing west, “front” means objects are west of me (x_object < x_agent). “Front-slight-left/right” for rubberduck/alarmclock implies I’m between their y-values, so y=0. Mid distances to both with x=2 then give x=5, which also matches pan being front-left at slightly-far range.
FINAL ANSWER:
(5...

✅ Evaluation Results

id: e006710c90936706

question: You move to a new location and face west. You observe: pan is at front-left, slightly far, facing forward; alarmclock is at front-slight-right, mid distance, facing forward; rubberduck is at front-slight-left, 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: [5, 0]

final_pos: [7, 11]

final_ori: [-1, 0]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'pan', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'alarmclock', 'direction': 'front-slight-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'rubberduck', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing right'}]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: fd2887aae65af114)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute vector from books (9,-1) to backpack (11,1): (2,2) → north-east; distance √8≈2.83 → mid distance.
FINAL ANSWER:
north-east, mid distance...

✅ Evaluation Results

id: fd2887aae65af114

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

action: None

answer: north east, mid distance

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 47e25a4138337ec9)

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

🤔 Assistant Answer (click to toggle)
THINK: Compute vector from pan to books: (+11, +2) which falls in the east bin; distance ≈11.2 which is in the far bin.
FINAL ANSWER:
east, far...

✅ Evaluation Results

id: 47e25a4138337ec9

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

action: None

answer: east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 05aacd3852716124)

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

🤔 Assistant Answer (click to toggle)
THINK: Compute vector from books (9,-1) to desklamp (-1,-7): (-10,-6) which is south-west; distance sqrt(136)≈11.7 falls in (8,16]→far.
FINAL ANSWER:
south-west, far...

✅ Evaluation Results

id: 05aacd3852716124

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

action: None

answer: south west, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: dd32b518994cc0cd)

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

🤔 Assistant Answer (click to toggle)
THINK: Agent is at rubberduck (2,-1) facing north; alarmclock is at (2,1), which is straight ahead by 2 units, so it is in front at near distance.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: dd32b518994cc0cd

question: Now you jump to rubberduck's direction, facing its direction. Describe where alarmclock 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_model_cogmap

Question 2 (ID: b3bd1bdceefdf76a)

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

🤔 Assistant Answer (click to toggle)
THINK: From red chair at (2,-7) facing south, shoppingcart at (4,-9) is 2 units left (east) and 2 units forward (south), giving a -45° bearing (front-left) at distance √8≈2.83 (mid distance).
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: b3bd1bdceefdf76a

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 9f4070bd1a76781d)

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

🤔 Assistant Answer (click to toggle)
THINK: After jumping to the laptop, you are at the laptop’s position and facing the laptop’s facing (east). The backpack is 1 unit to the east, which is directly in front and within the near distance bin.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 9f4070bd1a76781d

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

action: None

answer: front, near

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: e0bba9ba020fcf43)

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

🤔 Assistant Answer (click to toggle)
THINK: If alarmclock is front and near while facing left (west) relative to me, I must be facing north. The only north-facing object within 2 units directly in front of alarmclock is rubberduck (alarmclock is 2 units north of it).
FINAL ANSWER:
rubberduck...

✅ Evaluation Results

id: e0bba9ba020fcf43

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

action: None

answer:

answer: rubberduck

final_pos: [4, 10]

final_ori: [0, 1]

final_observation: [{'name': 'alarmclock', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}]

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 1

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 5ee18081cd74702f)

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

🤔 Assistant Answer (click to toggle)
THINK:
From the new view, backpack “facing left” implies I’m facing east (since backpack faces north). The only east-facing object consistent with backpack being directly in front (and airplane front-left) is the laptop.
FINAL ANSWER:
laptop...

✅ Evaluation Results

id: 5ee18081cd74702f

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

action: None

answer:

answer: laptop

final_pos: [12, 12]

final_ori: [1, 0]

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

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 3

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 48ef54e966ae6b2b)

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

🤔 Assistant Answer (click to toggle)
THINK: From earlier observations at the blue door, the bike lay front-left when facing west, placing it southwest of the blue door; the bike itself faces east, so from the bike facing east the blue door would appear front-left at a mid distance, matching the new observation.
FINAL ANSWER:
bike...

✅ Evaluation Results

id: 48ef54e966ae6b2b

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

action: None

answer:

answer: bike

final_pos: [4, 6]

final_ori: [1, 0]

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

object_positions:

shoes: [6.0, 10.0]

shoppingcart: [6.0, 2.0]

laptop: [12.0, 12.0]

rubberduck: [4.0, 10.0]

bike: [4.0, 6.0]

airplane: [14.0, 14.0]

pan: [2.0, 8.0]

alarmclock: [4.0, 12.0]

red chair: [4.0, 4.0]

backpack: [14.0, 12.0]

desklamp: [1.0, 3.0]

books: [12.0, 10.0]

yellow door: [8.0, 10.0]

blue door: [6.0, 7.0]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

room_id: 2

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: dd857cdb010f8d2c)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
From start face north → Rotate(90) face east.I previously saw the only “front-right, slightly far” object as the blue door, so I jump there. Rotate(-180) makes me face west. From the blue door while facing west, the “front-left, mid distance” object is the bike, so I jump to the bike (still f...

✅ Evaluation Results

id: dd857cdb010f8d2c

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

action: None

answer: front-left, slightly far

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 2 (ID: f648d1cc302e836a)

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

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

🤔 Assistant Answer (click to toggle)
THINK: After Rotate(90) (facing east), the front-right mid object is rubberduck. From the earlier proximity relations, shoes is west of the yellow door and near it. The subsequent JumpTo(front, mid) places the agent at the yellow door; then Rotate(-180) makes the agent face west, so shoes (west of t...

✅ Evaluation Results

id: f648d1cc302e836a

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

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 746bd0e0e6ba5a86)

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

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

🤔 Assistant Answer (click to toggle)
THINK: After rotating to face east, the front-left mid object is alarmclock; from there the front-right slightly-far object is yellow door. After rotating -180 at yellow door, shoes lie 45° to the front-right at ~1.4m.
FINAL ANSWER:
front-right, near...

✅ Evaluation Results

id: 746bd0e0e6ba5a86

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

action: None

answer: front, near

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: a64c44dde1456896)

❓ 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-right, slightly far, facing right

What action sequence led to this final view? The action sequence must be v...

🤔 Assistant Answer (click to toggle)
THINK: To see the bike “facing right,” the agent’s final facing must be north (so the bike’s east-facing orientation appears as “right”). A valid move-only sequence is to rotate to bring a previously observed jump target into FOV, jump there, then rotate back to north.
FINAL ANSWER:
Rotate(180), Jum...

✅ Evaluation Results

id: a64c44dde1456896

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-right, slightly far, facing right What action sequence led to this final view? The action sequence must be valid and only contain move actions. Answer format: <sequence of move actions> Example: JumpTo(lamp), Rotate(90)

action: None

answer:

final_pos: [1, 3]

final_ori: [0, 1]

room_id: 2

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

minimal_plan: [['rotate', 90], ['jumpto', 'blue door'], ['rotate', 180], ['jumpto', 'desklamp'], ['rotate', 90]]

final_observation: [{'name': 'bike', 'direction': 'front-right', 'distance': 'slightly far', 'orientation': 'facing right'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [1.0, 3.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: c61a5d692c908db6)

❓ 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 right; blue door is at front-right, mid distance, on front wall

What action sequence led to th...

🤔 Assistant Answer (click to toggle)
THINK:
Make blue door visible from the start by rotating east, jump to it, rotate to face the direction where desklamp was visible from the doorway, jump to desklamp (a plausible near-bike vantage), then rotate to align bike straight ahead and blue door front-right.
FINAL ANSWER:
Rotate(90), JumpTo(...

✅ Evaluation Results

id: c61a5d692c908db6

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 right; blue door is at front-right, 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: [4, 4]

final_ori: [0, 1]

room_id: 2

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

minimal_plan: [['rotate', 180], ['jumpto', 'blue door'], ['jumpto', 'red chair'], ['rotate', 180]]

final_observation: [{'name': 'bike', 'direction': 'front', 'distance': 'near', 'orientation': 'facing right'}, {'name': 'blue door', 'direction': 'front-right', 'distance': 'mid distance', 'orientation': 'on front wall'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [1.0, 3.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: 84f10bed2583426f)

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

🤔 Assistant Answer (click to toggle)
THINK: The described final view exactly matches what was seen from the yellow door after turning to face east; from the start (facing north), rotate to face east so the yellow door is in FOV, then jump to it.
FINAL ANSWER:
Rotate(90), JumpTo(yellow door)...

✅ Evaluation Results

id: 84f10bed2583426f

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

action: None

answer:

final_pos: [8, 10]

final_ori: [1, 0]

room_id: [1, 3]

init_pos: [2, 11]

init_ori: [0, 1]

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

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

final_observation: [{'name': 'airplane', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing right'}, {'name': 'backpack', 'direction': 'front-slight-left', 'distance': 'slightly far', 'orientation': 'facing left'}, {'name': 'laptop', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [8.0, 10.0]

final_ori: [1, 0]

visible_match: True

score: 1

evaluation_mode: use_model_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: d07c09cead29a5ab)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: backpack, books, shoppingcart, rubberduck.
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 agent position (0,0), x east, y north) and report the requested objects in order.
FINAL ANSWER:
(11, 1); (9, -1); (4, -9); (2, -1)...

✅ Evaluation Results

id: d07c09cead29a5ab

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

action: None

answer: [[12, 1], [10, -1], [4, -9], [2, -1]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.963

threshold: 0.900

score: 0.963

evaluation_mode: use_model_cogmap

Question 2 (ID: c301d9dd74a52e57)

❓ 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: laptop, alarmclock, desklamp, rubberduck, red chair.
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 with origin at the initial agent position (0,0), x east/right, y north/up, and list requested objects in the given order.
FINAL ANSWER:
(10, 1); (2, 1); (-1, -7); (2, -1); (2, -7)...

✅ Evaluation Results

id: c301d9dd74a52e57

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

action: None

answer: [[10, 1], [2, 1], [-1, -8], [2, -1], [2, -7]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.968

threshold: 0.900

score: 0.968

evaluation_mode: use_model_cogmap

Question 3 (ID: 4ade2017a10e41fb)

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

🤔 Assistant Answer (click to toggle)
THINK: Use the previously constructed global cognitive map coordinates (origin at initial agent pose) and report requested objects in the specified order.
FINAL ANSWER:
(9, -1); (4, -9); (10, 1); (2, 1); (1, -6)...

✅ Evaluation Results

id: 4ade2017a10e41fb

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

action: None

answer: [[10, -1], [4, -9], [10, 1], [2, 1], [2, -5]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.921

threshold: 0.900

score: 0.921

evaluation_mode: use_model_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: d41a4cb1c4ae88e8)

❓ 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: desklamp, rubberduck, shoppingcart.
...

🤔 Assistant Answer (click to toggle)
THINK: Compute bearings from start (0,0) facing north using the map coordinates; during CCW rotation bearings are encountered in decreasing order from 0 to -180 then from +180 down to 0.
FINAL ANSWER:
desklamp, shoppingcart, rubberduck...

✅ Evaluation Results

id: d41a4cb1c4ae88e8

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: desklamp, rubberduck, shoppingcart. List them in the exact order they appear directly ahead while you rotate. If two objects share a bearing, place the nearer one first. Answer format: <object_name1>, <object_name2>, ... Example: mug, sofa, plant

action: None

answer: ['desklamp', 'shoppingcart', 'rubberduck']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 35aed4a89a7998dd)

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

🤔 Assistant Answer (click to toggle)
THINK: Compute bearings from start (0,0) facing north: backpack ≈ +84.8°, rubberduck ≈ +116.6°, pan ≈ -146.3° (i.e., +213.7° clockwise). Clockwise sweep from 0° hits backpack, then rubberduck, then pan.
FINAL ANSWER:
backpack, rubberduck, pan...

✅ Evaluation Results

id: 35aed4a89a7998dd

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 070834d55928c793)

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

🤔 Assistant Answer (click to toggle)
THINK: Compute each object’s clockwise bearing from the origin with 0° at north (bearing = atan2(x,y)); then sort ascending by bearing for the clockwise rotation order.
FINAL ANSWER:
backpack, rubberduck, shoppingcart...

✅ Evaluation Results

id: 070834d55928c793

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

action: None

answer: ['backpack', 'rubberduck', 'shoppingcart']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 12e5714b691ffd9d)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (4, -2) and face north.
What is the egocentric relation of shoes?

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (4,-2) facing north; shoes at (4,-1) is directly ahead by 1 unit, which bins to front and near.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: 12e5714b691ffd9d

question: Still treat your initial position as origin (0, 0) You move to (4, -2) and face north. What is the egocentric relation of shoes? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front, near

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 37203b723cb2ed85)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (4, 2) and face south.
What is the egocentric relation of rubberduck?

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

🤔 Assistant Answer (click to toggle)
THINK: From agent at (4,2) facing south, rubberduck at (2,-1) is 2 left and 3 forward; angle ≈ 34° to the left (front-left) and distance √13 ≈ 3.6 (mid distance).
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: 37203b723cb2ed85

question: Still treat your initial position as origin (0, 0) You move to (4, 2) and face south. What is the egocentric relation of rubberduck? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 3 (ID: 1e0d4b92e81bdd63)

❓ Evaluation Question (click to toggle)
Still treat your initial position as origin (0, 0)
You move to (5, 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 (5,0) facing west, pan at (-2,-3) is mostly in front and slightly to the left; distance is about 7.6 so slightly far.
FINAL ANSWER:
front-left, slightly far...

✅ Evaluation Results

id: 1e0d4b92e81bdd63

question: Still treat your initial position as origin (0, 0) You move to (5, 0) and face west. What is the egocentric relation of pan? Answer format: <direction>, <distance> Example: front, near

action: None

answer: front-left, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 7eb7d98b38d92500)

❓ Evaluation Question (click to toggle)
You move to a new location and face north.
You observe: shoes is at front, near, facing left; alarmclock is at front-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)
...

🤔 Assistant Answer (click to toggle)
THINK: Shoes being “front, near” while facing north implies same x as me and 1–2 units ahead; using shoes at (4,-1) gives candidate positions (4,-2) or (4,-3). Checking alarmclock at (2,1): from (4,-2) it is (-2,+3) with distance ≈3.6 (mid) and bearing ≈-34° (front-left), which matches; (4,-3) would...

✅ Evaluation Results

id: 7eb7d98b38d92500

question: You move to a new location and face north. You observe: shoes is at front, near, facing left; alarmclock is at front-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: [4, -2]

final_pos: [6, 9]

final_ori: [0, 1]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'shoes', 'direction': 'front', 'distance': 'near', 'orientation': 'facing left'}, {'name': 'alarmclock', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing left'}]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 4190e9e80fa5f994)

❓ Evaluation Question (click to toggle)
You move to a new location and face south.
You observe: rubberduck is at front-right, mid distance, facing backward; blue door is at front, slightly far, on front wall; pan is at front-right, slightly far, facing right
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate...

🤔 Assistant Answer (click to toggle)
THINK: Blue door is observed exactly “front” while facing south, so my x must match the blue door’s x and I must be 4–8 units north of it. Using earlier-consistent global placement blue door at (4,-4) gives my x=4 and y in {1,2} (since distance is slightly far). Rubberduck at (2,-1) must be front-ri...

✅ Evaluation Results

id: 4190e9e80fa5f994

question: You move to a new location and face south. You observe: rubberduck is at front-right, mid distance, facing backward; blue door is at front, slightly far, on front wall; pan is at front-right, slightly far, 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: [4, 2]

final_pos: [6, 13]

final_ori: [0, -1]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

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

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: e006710c90936706)

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

🤔 Assistant Answer (click to toggle)
THINK: Use the known global coordinates of alarmclock (2,1) and rubberduck (2,-1); find the unique integer agent position facing west that places them at mid distance with slight-right/left bearings, then verify pan fits front-left slightly-far.
FINAL ANSWER:
(5, 0)...

✅ Evaluation Results

id: e006710c90936706

question: You move to a new location and face west. You observe: pan is at front-left, slightly far, facing forward; alarmclock is at front-slight-right, mid distance, facing forward; rubberduck is at front-slight-left, 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: [5, 0]

final_pos: [7, 11]

final_ori: [-1, 0]

room_id: 1

object_positions:

shoes: [6, 10]

shoppingcart: [6, 2]

laptop: [12, 12]

rubberduck: [4, 10]

bike: [4, 6]

airplane: [14, 14]

pan: [2, 8]

alarmclock: [4, 12]

red chair: [4, 4]

backpack: [14, 12]

desklamp: [1, 3]

books: [12, 10]

yellow door: [8, 10]

blue door: [6, 7]

object_orientations:

shoes: [-1, 0]

shoppingcart: [0, -1]

laptop: [1, 0]

rubberduck: [0, 1]

bike: [1, 0]

airplane: [0, -1]

pan: [-1, 0]

alarmclock: [-1, 0]

red chair: [0, -1]

backpack: [0, 1]

desklamp: [0, -1]

yellow door: [1, 0]

blue door: [0, 1]

final_observation: [{'name': 'pan', 'direction': 'front-left', 'distance': 'slightly far', 'orientation': 'facing forward'}, {'name': 'alarmclock', 'direction': 'front-slight-right', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'rubberduck', 'direction': 'front-slight-left', 'distance': 'mid distance', 'orientation': 'facing right'}]

gate_info:

yellow door:

room_ids: [1, 3]

ori_by_room:

1: [-1, 0]

3: [1, 0]

blue door:

room_ids: [2, 1]

ori_by_room:

2: [0, -1]

1: [0, 1]

object_rooms:

shoes: 1

shoppingcart: 2

laptop: 3

rubberduck: 1

bike: 2

airplane: 3

pan: 1

alarmclock: 1

red chair: 2

backpack: 3

desklamp: 2

books: 3

yellow door: [1, 3]

blue door: [2, 1]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_model_cogmap

Model: gpt-5.2

sample_run05

Select Configuration:

📊 Sample Metrics

🔍 Exploration

✅ Evaluation

✅ Evaluation (prompt_cogmap)

✅ Evaluation (use_gt_cogmap)

✅ Evaluation (use_model_cogmap)

🧠 Cognitive Map

🧭 False Belief CogMap

📊 Performance Charts

Cognitive Map (Update)

Cognitive Map (Full)

Cognitive Map (Self-Tracking)

FB CogMap (Unchanged)

Fog Probe F1

Fog Probe Precision

Fog Probe Recall

Position Update

Facing Update

Position Stability

Facing Stability

🌍 Exploration Phase

🔄 Turn 1

🔄 Turn 2

🔄 Turn 3

🔄 Turn 4

🔄 Turn 5

🔄 Turn 6

🔄 Turn 7

🔄 Turn 8

🔄 Turn 9

🧭 False Belief Exploration

🔄 FB Turn 10

🔄 FB Turn 11

🔄 FB Turn 12

🔄 FB Turn 13

🔄 FB Turn 14

📊 Evaluation (default)

📊 Task: DirectionEvaluationTask

Question 1 (ID: fd2887aae65af114)

Question 2 (ID: 47e25a4138337ec9)

Question 3 (ID: 05aacd3852716124)

📊 Task: PovEvaluationTask

Question 1 (ID: dd32b518994cc0cd)

Question 2 (ID: b3bd1bdceefdf76a)

Question 3 (ID: 9f4070bd1a76781d)

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: e0bba9ba020fcf43)

Question 2 (ID: 5ee18081cd74702f)

Question 3 (ID: 48ef54e966ae6b2b)

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: 746bd0e0e6ba5a86)

Question 2 (ID: dd857cdb010f8d2c)

Question 3 (ID: f648d1cc302e836a)

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: a64c44dde1456896)

Question 2 (ID: c61a5d692c908db6)

Question 3 (ID: 84f10bed2583426f)

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: d07c09cead29a5ab)

Question 2 (ID: c301d9dd74a52e57)

Question 3 (ID: 4ade2017a10e41fb)

📊 Task: RotEvaluationTask

Question 1 (ID: d41a4cb1c4ae88e8)

Question 2 (ID: 35aed4a89a7998dd)

Question 3 (ID: 070834d55928c793)

📊 Task: RotDualEvaluationTask

Question 1 (ID: 51578be02150882b)

Question 2 (ID: a232cb4d3c853647)

Question 3 (ID: 83fd00114d015923)

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 12e5714b691ffd9d)

Question 2 (ID: 37203b723cb2ed85)

Question 3 (ID: 1e0d4b92e81bdd63)

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 7eb7d98b38d92500)

Question 2 (ID: 4190e9e80fa5f994)

Question 3 (ID: e006710c90936706)