SpatialGym Dashboard

🔄 Turn 1

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

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

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

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

🤔 Assistant Thinking (click to toggle)
Start by observing from the initial position (origin) facing north to identify visible objects/doors and plan efficient jumps to doorways for multi-room visibility....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Place agent at origin facing north; assign integer coordinates consistent with observed egocentric bearings and distance bins, and include facing for the truck.
FINAL ANSWER:
{
"agent": { "position": [0, 0], "facing": "north" },
"truck": { "position": [-2, 3], "facing": "east" },
"red d...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -2,
      3
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      2,
      5
    ]
  }
}

🎯 Ground Truth (Observed)

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

🎯 Ground Truth (Full)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      -3,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Assign integer coordinates consistent with the egocentric bearings/distances: truck in front-left at mid distance, red door in front-right at slightly-far distance on the front wall; encode truck facing as local +x (right).
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"truck": {
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "truck": {
      "position": [
        -2,
        3
      ],
      "facing": "+x"
    },
    "red door": {
      "position": [
        3,
        5
      ]
    }
  }
}

🎯 Ground Truth

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

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "truck": {
      "position": [
        -2,
        3
      ],
      "facing": "+x"
    },
    "red door": {
      "position": [
        3,
        5
      ]
    }
  }
}

🎯 Ground Truth

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

🌫️ Fog Probe Response (click to toggle)
THINK: Only the forward-facing area from the start has been observed; points behind/to the side (A, B, C) and points beyond the internal wall/other rooms (E, F) remain unobserved. Point D lies in the forward area and is likely within the observed region.
FINAL ANSWER:
{"unexplored":["A","B","C","E",...

🗺️ Symbolic Fog Map

################
#########......#
#########......#
#########......#
#########......#
#########......#
#########......#
#############+##
#......#..EF..##
#......#......##
#......#D.....##
#......#.*....##
#......+.B.C..##
#......#A.....##
################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, E, F

🎯 Ground Truth: C, A, B

🧠 Cognitive Map Metrics

Global:

dir: 0.500

facing: 1

pos: 0.788

overall: 0.763

Global (Full):

dir: 0.025

facing: 0.200

pos: 0.158

overall: 0.128

Local:

dir: 1

facing: 1

pos: 0.802

overall: 0.934

Local (Newly):

dir: 1

facing: 1

pos: 0.802

overall: 0.934

Fog Probe:

precision: 0.600

recall: 1

overall: 0.750

📈 Turn Metrics

node_coverage: 0.143

edge_coverage: 0.049

is_action_fail: False

step: 1

action_counts:

move: 0

rotate: 0

return: 0

observe: 1

term: 0

forced_term: 0

query: 0

information_gain: 0.098

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 2

👤 Environment Observation (click to toggle)
You observe:
• truck: front-left, mid distance, facing right
• red door: front-right, slightly far, on front wall.
You have a maximum of 19 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
Jump to the red door (already observed and in FOV). From the doorway, an Observe should reveal objects in both the current room and the connected room, maximizing new information per cost....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assign integer coordinates consistent with egocentric bearings/distances from the initial origin, keep jump-to-door position fixed, and satisfy the stated proximity relations (TV south of wine; airplane east of wine).
FINAL ANSWER:
{
"agent": { "position": [4, 5], "facing": "north" },
"tr...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      4,
      5
    ],
    "facing": "north"
  },
  "truck": {
    "position": [
      -2,
      2
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      5
    ]
  },
  "wine": {
    "position": [
      1,
      9
    ]
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      3,
      9
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      6,
      7
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Observed)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      -3,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Assign integer relative coordinates consistent with each object’s egocentric bearing/distance bins and the stated near cardinal relations (televison south of wine; airplane east of wine). Use local axes with agent at origin facing +y.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"r...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "red door": {
      "position": [
        0,
        0
      ]
    },
    "wine": {
      "position": [
        -3,
        5
      ]
    },
    "televison": {
      "position": [
        -3,
        4
      ],
      "facing": "-x"
    },
    "airplane": {
      "position": [
        -2,
        5
      ],
      "facing": "-x"
    },
    "shoppingcart": {
      "position": [
        2,
        3
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "shoppingcart": {
    "position": [
      1,
      2
    ],
    "facing": "south"
  },
  "televison": {
    "position": [
      -3,
      4
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      -1,
      6
    ],
    "facing": "west"
  },
  "wine": {
    "position": [
      -3,
      6
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "red door": {
      "position": [
        0,
        0
      ]
    },
    "wine": {
      "position": [
        -3,
        5
      ]
    },
    "televison": {
      "position": [
        -3,
        4
      ],
      "facing": "-x"
    },
    "airplane": {
      "position": [
        -2,
        5
      ],
      "facing": "-x"
    },
    "shoppingcart": {
      "position": [
        2,
        3
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "shoppingcart": {
    "position": [
      1,
      2
    ],
    "facing": "south"
  },
  "televison": {
    "position": [
      -3,
      4
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      -1,
      6
    ],
    "facing": "west"
  },
  "wine": {
    "position": [
      -3,
      6
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: Candidates in the lower-right area (south of my current doorway position) are in a region I have not observed yet; candidates in the upper-right area are within the room I just observed into.
FINAL ANSWER:
{
"unexplored": ["A", "B", "E", "I", "J", "K"]
}...

🗺️ Symbolic Fog Map

################
#########.C....#
#########L.....#
#########..F...#
#########......#
#########H.....#
#########G.D...#
#############*##
#......#......##
#......#.BJE..##
#......#....K.##
#......#......##
#......+..A...##
#......#.....I##
################

🔍 Fog Probe JSONs

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

🤖 Predicted: A, B, E, I, J, K

🎯 Ground Truth: I, K, A, D, G, H

🧠 Cognitive Map Metrics

Global:

dir: 0.786

facing: 1

pos: 0.856

overall: 0.881

Global (Full):

dir: 0.183

facing: 0.500

pos: 0.399

overall: 0.361

Local:

dir: 0.800

facing: 1

pos: 0.840

overall: 0.880

Local (Newly):

dir: 0.800

facing: 1

pos: 0.840

overall: 0.880

Fog Probe:

precision: 0.500

recall: 0.500

overall: 0.500

📈 Turn Metrics

node_coverage: 0.429

edge_coverage: 0.146

is_action_fail: False

step: 2

action_counts:

move: 1

rotate: 0

return: 0

observe: 2

term: 0

forced_term: 0

query: 0

information_gain: 0.215

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 3

👤 Environment Observation (click to toggle)
You jumped to red door. You observe:
• televison: front-left, slightly far, facing left
• airplane: front-slight-left, slightly far, facing left
• wine: front-left, slightly far
• shoppingcart: front-right, mid distance, facing backward
Assume your current facing direction is called "north":
• tele...

🤔 Assistant Thinking (click to toggle)
From the red doorway, rotate to expand coverage and try to spot additional objects/another door (green door) without spending a jump. Then observe....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Place all observed entities on an integer grid consistent with the given egocentric bearings/distances and the doorway-relative proximity relations (televison–wine–airplane cluster), keeping the agent at the red door after the final rotation (facing east).
FINAL ANSWER:
{
"agent": { "positi...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      3,
      6
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      3,
      6
    ]
  },
  "truck": {
    "position": [
      -2,
      3
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      0,
      11
    ],
    "facing": "west"
  },
  "wine": {
    "position": [
      0,
      12
    ]
  },
  "airplane": {
    "position": [
      1,
      12
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      5,
      9
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Observed)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      4
    ],
    "facing": "east"
  }
}

🎯 Ground Truth (Full)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      -3,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      4
    ],
    "facing": "east"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: No objects are currently in my 90° FOV after rotating, so the local cognitive map contains an empty objects set relative to my current pose.
FINAL ANSWER:
{
"origin": "agent",
"objects": {}
}...

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

📊 Cognitive Map JSONs

🤖 Predicted

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

🎯 Ground Truth

(no data)

🌫️ Fog Probe Response (click to toggle)
THINK: We have only observed a small subset of the environment around the start and the red doorway; all candidate points A-L shown on the probe map lie in regions not yet covered by our observations, so they should be marked unexplored.
FINAL ANSWER:
{"unexplored":["A","B","C","D","E","F","G","H",...

🗺️ Symbolic Fog Map

################
#########......#
#########......#
#########..D..C#
#########......#
#########BIG...#
#########.L....#
#############*##
#......#....K.##
#......#F.....##
#......#..A.H.##
#......#......##
#......+....E.##
#......#...J..##
################

🔍 Fog Probe JSONs

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

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

🎯 Ground Truth: J, H, E, I, L, B

🧠 Cognitive Map Metrics

Global:

dir: 0.857

facing: 1

pos: 0.677

overall: 0.845

Global (Full):

dir: 0.200

facing: 0.500

pos: 0.316

overall: 0.339

Local:

(none)

Local (Newly):

(none)

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.429

edge_coverage: 0.146

is_action_fail: False

step: 3

action_counts:

move: 1

rotate: 1

return: 0

observe: 3

term: 0

forced_term: 0

query: 0

information_gain: 0.215

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 4

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. 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)
I’m at the red doorway but currently facing east (seeing nothing). Rotate another 90° clockwise to face south (back into room 1) and observe to recover visibility and look for the green door/other objects....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a consistent integer-coordinate global map using the initial pose as origin, placing each observed object to satisfy the given egocentric direction bins, distance bins, doorway jump, and the stated proximity relations (tv south of wine; airplane east of wine). Convert object facings fro...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      3,
      5
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      3,
      5
    ]
  },
  "truck": {
    "position": [
      -2,
      3
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      9
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      2,
      10
    ],
    "facing": "west"
  },
  "wine": {
    "position": [
      1,
      10
    ]
  },
  "shoppingcart": {
    "position": [
      5,
      8
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      1,
      1
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      2,
      1
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      3,
      0
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Observed)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      4
    ],
    "facing": "south"
  }
}

🎯 Ground Truth (Full)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      -3,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red 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 from current FOV only; approximate integer positions consistent with the reported egocentric bearings and “slightly far” distance bin, and encode each object’s facing in the same local axes.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"chair": {
...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "chair": {
      "position": [
        2,
        5
      ],
      "facing": "-y"
    },
    "backpack": {
      "position": [
        1,
        5
      ],
      "facing": "+y"
    },
    "shoes": {
      "position": [
        0,
        5
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "backpack": {
    "position": [
      1,
      4
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      3,
      6
    ],
    "facing": "north"
  },
  "shoes": {
    "position": [
      0,
      6
    ],
    "facing": "north"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "chair": {
      "position": [
        2,
        5
      ],
      "facing": "-y"
    },
    "backpack": {
      "position": [
        1,
        5
      ],
      "facing": "+y"
    },
    "shoes": {
      "position": [
        0,
        5
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "backpack": {
    "position": [
      1,
      4
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      3,
      6
    ],
    "facing": "north"
  },
  "shoes": {
    "position": [
      0,
      6
    ],
    "facing": "north"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: We have only observed limited sectors around the red door and nearby objects; none of the candidate-labeled frontier points have been directly observed/covered. All labeled points appear to lie in still-unmapped open regions beyond current FOV coverage.
FINAL ANSWER:
{
"unexplored": ["A", ...

🗺️ Symbolic Fog Map

################
#########....G.#
#########......#
#########......#
#########......#
#########F..BH.#
#########.EA...#
#############*##
#......#...C..##
#......#......##
#......#......##
#......#D.....##
#......+......##
#......#......##
################

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F, G, H

🤖 Predicted: A, B, C, D, E, F, G, H

🎯 Ground Truth: D, A, E, F

🧠 Cognitive Map Metrics

Global:

dir: 0.800

facing: 1

pos: 0.771

overall: 0.857

Global (Full):

dir: 0.367

facing: 0.800

pos: 0.514

overall: 0.560

Local:

dir: 0.500

facing: 1

pos: 0.835

overall: 0.778

Local (Newly):

dir: 0.500

facing: 1

pos: 0.835

overall: 0.778

Fog Probe:

precision: 0.500

recall: 1

overall: 0.667

📈 Turn Metrics

node_coverage: 0.643

edge_coverage: 0.220

is_action_fail: False

step: 4

action_counts:

move: 1

rotate: 2

return: 0

observe: 4

term: 0

forced_term: 0

query: 0

information_gain: 0.299

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 5

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• chair: front-right, slightly far, facing backward
• backpack: front-slight-right, slightly far, facing forward
• shoes: front, slightly far, facing backward.
You have a maximum of 16 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts...

🤔 Assistant Thinking (click to toggle)
We still haven’t located the green door (needed to reach room 3) and several objects remain unseen (bike, scooter, basket, table). From the red doorway facing south, rotate 90° clockwise to face west and observe to scan the remaining part of this room/connected doorway area for the green door or new...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Assign consistent integer coordinates relative to the initial origin (0,0), using observed bearings/distances and the fact that JumpTo(red door) places the agent at the red door location; ensure object facings are cardinal and consistent across views.
FINAL ANSWER:
{
"agent": { "position": ...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      4,
      5
    ],
    "facing": "west"
  },
  "truck": {
    "position": [
      -2,
      3
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      5
    ]
  },
  "televison": {
    "position": [
      1,
      9
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      2,
      10
    ],
    "facing": "west"
  },
  "wine": {
    "position": [
      1,
      10
    ]
  },
  "shoppingcart": {
    "position": [
      6,
      7
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      1,
      1
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      0
    ],
    "facing": "north"
  },
  "green door": {
    "position": [
      -1,
      2
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      4,
      4
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      -3,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red 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 using the current FOV only (truck, green door). Encode relative positions on the agent’s local axes (+y forward, +x right) and include facing for the truck.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"truck": {
"position": [-1, 5],
"f...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "truck": {
      "position": [
        -1,
        5
      ],
      "facing": "-y"
    },
    "green door": {
      "position": [
        -2,
        5
      ]
    }
  }
}

🎯 Ground Truth

{
  "truck": {
    "position": [
      -2,
      5
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -5,
      6
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "truck": {
      "position": [
        -1,
        5
      ],
      "facing": "-y"
    },
    "green door": {
      "position": [
        -2,
        5
      ]
    }
  }
}

🎯 Ground Truth

{
  "green door": {
    "position": [
      -5,
      6
    ],
    "facing": "east"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.803

facing: 1

pos: 0.770

overall: 0.858

Global (Full):

dir: 0.442

facing: 0.800

pos: 0.565

overall: 0.602

Local:

dir: 0.667

facing: 1

pos: 0.694

overall: 0.787

Local (Newly):

dir: 0

facing: 1

pos: 0.611

overall: 0.537

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 0.714

edge_coverage: 0.268

is_action_fail: False

step: 5

action_counts:

move: 1

rotate: 3

return: 0

observe: 5

term: 0

forced_term: 0

query: 0

information_gain: 0.357

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 6

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• truck: front-slight-left, slightly far, facing backward
• green door: front-left, slightly far, on front wall.
You have a maximum of 15 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [...

🤔 Assistant Thinking (click to toggle)
Jump to the newly spotted green door to gain doorway visibility into its connected room(s), aiming to reveal remaining unseen objects (bike, scooter, basket, table) efficiently....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single global integer-coordinate map consistent with all egocentric observations, rotations, doorway jumps, and the stated near-relations; set agent at green door facing west (after rotations), and assign object facings by converting relative facings at observation time into global ca...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      2
    ],
    "facing": "west"
  },
  "red door": {
    "position": [
      3,
      5
    ]
  },
  "green door": {
    "position": [
      -2,
      2
    ]
  },
  "truck": {
    "position": [
      -2,
      3
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      9
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      2,
      10
    ],
    "facing": "west"
  },
  "wine": {
    "position": [
      1,
      10
    ]
  },
  "shoppingcart": {
    "position": [
      5,
      8
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      1,
      1
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      2,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      3,
      0
    ],
    "facing": "north"
  },
  "scooter": {
    "position": [
      -7,
      5
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -5,
      1
    ]
  },
  "basket": {
    "position": [
      -5,
      3
    ]
  }
}

🎯 Ground Truth (Observed)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -2,
      -1
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Full)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      -3,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -2,
      -1
    ],
    "facing": "west"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map from the current viewpoint at the green door, assigning integer coordinates consistent with the observed left/right and distance bins, and include facings where given.
FINAL ANSWER:
{
"origin": "agent",
"objects": {
"green door": {
"position": [...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green door": {
      "position": [
        0,
        0
      ]
    },
    "table": {
      "position": [
        -1,
        3
      ]
    },
    "basket": {
      "position": [
        1,
        3
      ]
    },
    "scooter": {
      "position": [
        4,
        4
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "basket": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -1,
      3
    ],
    "facing": "east"
  },
  "scooter": {
    "position": [
      3,
      5
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "green door": {
      "position": [
        0,
        0
      ]
    },
    "table": {
      "position": [
        -1,
        3
      ]
    },
    "basket": {
      "position": [
        1,
        3
      ]
    },
    "scooter": {
      "position": [
        4,
        4
      ],
      "facing": "-y"
    }
  }
}

🎯 Ground Truth

{
  "basket": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  },
  "table": {
    "position": [
      -1,
      3
    ],
    "facing": "east"
  },
  "scooter": {
    "position": [
      3,
      5
    ],
    "facing": "east"
  }
}

🌫️ Fog Probe Response (click to toggle)
THINK: The candidates A, B, C, D, and E appear to lie in the left-side partitioned area behind the internal wall segments, which we have not yet directly observed/covered; F is adjacent to the agent’s current known area.
FINAL ANSWER:
{
"unexplored": ["A", "B", "C", "D", "E"]
}...

🗺️ Symbolic Fog Map

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

🔍 Fog Probe JSONs

📍 Candidates: A, B, C, D, E, F

🤖 Predicted: A, B, C, D, E

🎯 Ground Truth: D, C, E

🧠 Cognitive Map Metrics

Global:

dir: 0.790

facing: 1

pos: 0.706

overall: 0.832

Global (Full):

dir: 0.692

facing: 0.900

pos: 0.659

overall: 0.750

Local:

dir: 0.667

facing: 1

pos: 0.881

overall: 0.849

Local (Newly):

dir: 0.667

facing: 1

pos: 0.881

overall: 0.849

Fog Probe:

precision: 0.600

recall: 1

overall: 0.750

📈 Turn Metrics

node_coverage: 0.929

edge_coverage: 0.341

is_action_fail: False

step: 6

action_counts:

move: 2

rotate: 3

return: 0

observe: 6

term: 0

forced_term: 0

query: 0

information_gain: 0.431

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 7

👤 Environment Observation (click to toggle)
You jumped to green door. You observe:
• scooter: front-right, slightly far, facing backward
• basket: front-slight-right, mid distance
• table: front-slight-left, mid distance
Assume your current facing direction is called "north":
• basket is east, near to table.
You have a maximum of 14 explorat...

🤔 Assistant Thinking (click to toggle)
Only the bike remains unobserved. From the green doorway, rotate to a new heading to scan the other visible sector/connected room area and try to bring the bike into FOV....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single consistent global grid (origin at initial agent position) by assigning integer coordinates that satisfy all observed egocentric bearings/distances and the reported near/proximity relations; convert each object’s facing from egocentric (at time of observation) into global cardin...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -3,
      0
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      3,
      4
    ]
  },
  "green door": {
    "position": [
      -3,
      0
    ]
  },
  "truck": {
    "position": [
      -2,
      3
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      0,
      8
    ],
    "facing": "west"
  },
  "wine": {
    "position": [
      0,
      9
    ]
  },
  "airplane": {
    "position": [
      1,
      9
    ],
    "facing": "west"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "chair": {
    "position": [
      0,
      0
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      2,
      -1
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      3,
      -1
    ],
    "facing": "north"
  },
  "table": {
    "position": [
      -6,
      -1
    ]
  },
  "basket": {
    "position": [
      -6,
      1
    ]
  },
  "scooter": {
    "position": [
      -8,
      2
    ],
    "facing": "east"
  },
  "bike": {
    "position": [
      -4,
      3
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Observed)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      -3,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -2,
      -1
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      -3,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -2,
      -1
    ],
    "facing": "north"
  }
}

🗺️ Local Cognitive Map Response (click to toggle)
THINK: Build a local (agent-centric) map using the current FOV only (truck, bike). Convert bearings/distances into consistent integer coordinates that satisfy the observed proximity: truck is east and near to bike.
FINAL ANSWER:
```json
{
"origin": "agent",
"objects": {
"bike": { "position":...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "bike": {
      "position": [
        -1,
        3
      ],
      "facing": "-x"
    },
    "truck": {
      "position": [
        1,
        3
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "bike": {
    "position": [
      -1,
      3
    ],
    "facing": "west"
  },
  "truck": {
    "position": [
      1,
      3
    ],
    "facing": "east"
  }
}

📊 Cognitive Map JSONs

🤖 Predicted

{
  "origin": "agent",
  "objects": {
    "bike": {
      "position": [
        -1,
        3
      ],
      "facing": "-x"
    },
    "truck": {
      "position": [
        1,
        3
      ],
      "facing": "+x"
    }
  }
}

🎯 Ground Truth

{
  "bike": {
    "position": [
      -1,
      3
    ],
    "facing": "west"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.875

facing: 1

pos: 0.799

overall: 0.891

Global (Full):

dir: 0.875

facing: 1

pos: 0.799

overall: 0.891

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: 0.390

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: 0.619

reward: -0.100

is_done: False

is_valid_action: True

🔄 Turn 8

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe:
• truck: front-slight-right, mid distance, facing right
• bike: front-slight-left, mid distance, facing left
Assume your current facing direction is called "north":
• truck is east, near to bike.
You have a maximum of 13 exploration steps left.
Strictly follo...

🤔 Assistant Thinking (click to toggle)
All listed objects and both doors have now been observed; exploration can end to minimize further cost....

💬 Assistant Action
Actions: [Term()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a globally consistent integer-coordinate map using the initial origin frame (agent start at [0,0], facing north), assigning coordinates that satisfy all observed relative bearings/distances and near-relations; set agent final pose at termination (at green door, facing north).
FINAL ANSW...

📊 Cognitive Map JSONs

🤖 Predicted

{
  "agent": {
    "position": [
      -2,
      0
    ],
    "facing": "north"
  },
  "red door": {
    "position": [
      3,
      5
    ]
  },
  "green door": {
    "position": [
      -2,
      0
    ]
  },
  "truck": {
    "position": [
      -1,
      3
    ],
    "facing": "east"
  },
  "chair": {
    "position": [
      -2,
      -1
    ],
    "facing": "north"
  },
  "backpack": {
    "position": [
      1,
      -2
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      3,
      -2
    ],
    "facing": "north"
  },
  "televison": {
    "position": [
      1,
      9
    ],
    "facing": "west"
  },
  "airplane": {
    "position": [
      2,
      10
    ],
    "facing": "west"
  },
  "wine": {
    "position": [
      1,
      10
    ]
  },
  "shoppingcart": {
    "position": [
      5,
      7
    ],
    "facing": "south"
  },
  "scooter": {
    "position": [
      -7,
      3
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      1
    ]
  },
  "table": {
    "position": [
      -5,
      -1
    ]
  },
  "bike": {
    "position": [
      -3,
      3
    ],
    "facing": "west"
  }
}

🎯 Ground Truth (Observed)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      -3,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -2,
      -1
    ],
    "facing": "north"
  }
}

🎯 Ground Truth (Full)

{
  "truck": {
    "position": [
      -1,
      2
    ],
    "facing": "east"
  },
  "televison": {
    "position": [
      1,
      8
    ],
    "facing": "west"
  },
  "bike": {
    "position": [
      -3,
      2
    ],
    "facing": "west"
  },
  "chair": {
    "position": [
      1,
      -2
    ],
    "facing": "north"
  },
  "airplane": {
    "position": [
      3,
      10
    ],
    "facing": "west"
  },
  "scooter": {
    "position": [
      -7,
      2
    ],
    "facing": "east"
  },
  "backpack": {
    "position": [
      3,
      0
    ],
    "facing": "south"
  },
  "shoes": {
    "position": [
      4,
      -2
    ],
    "facing": "north"
  },
  "wine": {
    "position": [
      1,
      10
    ],
    "facing": "east"
  },
  "basket": {
    "position": [
      -5,
      0
    ],
    "facing": "east"
  },
  "shoppingcart": {
    "position": [
      5,
      6
    ],
    "facing": "south"
  },
  "table": {
    "position": [
      -5,
      -2
    ],
    "facing": "east"
  },
  "green door": {
    "position": [
      -2,
      -1
    ],
    "facing": "east"
  },
  "red door": {
    "position": [
      4,
      4
    ],
    "facing": "north"
  },
  "agent": {
    "position": [
      -2,
      -1
    ],
    "facing": "north"
  }
}

🧠 Cognitive Map Metrics

Global:

dir: 0.792

facing: 1

pos: 0.799

overall: 0.864

Global (Full):

dir: 0.792

facing: 1

pos: 0.799

overall: 0.864

Local:

(none)

Local (Newly):

(none)

Fog Probe:

(none)

📈 Turn Metrics

node_coverage: 1

edge_coverage: 0.390

is_action_fail: False

step: 8

action_counts:

move: 2

rotate: 4

return: 0

observe: 7

term: 1

forced_term: 0

query: 0

information_gain: 0.619

reward: -0.100

is_done: True

is_valid_action: True

🔄 FB Turn 9

👤 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 with a baseline Observe() from the initial pose (origin, facing north) to detect any immediate changes (truck/doors visibility, bearings, distances, orientations) and to decide the most informative next jump (likely to a door)....

💬 Assistant Action
Actions: [Observe()]

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single consistent global grid map anchored at the initial agent pose (0,0) facing north, using integer coordinates that satisfy the observed relative bearings/distances and the within-room proximity constraints (wine–televison–airplane cluster; table–basket; bike–truck).
FINAL ANSWER:...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.318

facing: 0.571

pos: 0.385

overall: 0.425

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'basket', 'bike', 'chair', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck', 'wine']

all_changed_objects: ['airplane', 'bike', 'chair', 'wine']

newly_observed_changed (this turn): []

unchanged_objects: ['backpack', 'basket', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'backpack', 'basket', 'bike', 'chair', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck', 'wine']

🧭 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': 'chair', 'ori': True}, {'name': 'airplane', 'ori': True}, {'name': 'wine', 'pos': True}, {'name': 'bike', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

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

🔄 FB Turn 10

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

🤔 Assistant Thinking (click to toggle)
Jump to the red door to re-observe both connected rooms from the doorway and compare the set/relations (televison/airplane/wine/shoppingcart) against the prior map to detect changes efficiently....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Construct a single consistent global grid map (origin at initial agent position) that satisfies all observed relative bearings/distances and includes every listed object and both doors; update airplane’s facing to match the latest observation.
FINAL ANSWER:
{
"agent": { "position": [3, 5], ...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: airplane:

dir: None

pos: None

facing: 1

overall: None

Retention: airplane:

dir: None

pos: None

facing: 0

overall: None

Unchanged (all):

dir: 0.924

facing: 1

pos: 0.780

overall: 0.901

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'basket', 'bike', 'chair', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck', 'wine']

all_changed_objects: ['airplane', 'bike', 'chair', 'wine']

newly_observed_changed (this turn): ['airplane']

unchanged_objects: ['backpack', 'basket', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck']

pred_changed_keys: ['airplane']

pred_unchanged_keys: ['airplane', 'backpack', 'basket', 'bike', 'chair', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck', 'wine']

🧭 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': 'chair', 'ori': True}, {'name': 'airplane', 'ori': True}, {'name': 'wine', 'pos': True}, {'name': 'bike', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['airplane']

newly_observed_unchanged_objects: ['televison', 'shoppingcart']

🔄 FB Turn 11

👤 Environment Observation (click to toggle)
You jumped to red door. You observe:
• televison: front-left, slightly far, facing left
• airplane: front-slight-left, slightly far, facing forward
• shoppingcart: front-right, mid distance, facing backward.
You have a maximum of 13 exploration steps left.
Strictly follow this format:
THINK:
[Your ...

🤔 Assistant Thinking (click to toggle)
At the red doorway, compare the rest of the scene by scanning other headings; first rotate clockwise to check the sector that was previously empty and see if any moved object (e.g., wine) appears there now....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I must output a complete global JSON map (agent, all objects, both doors) with integer coordinates relative to the initial origin. Since only binned/relative observations were provided (no Query coordinates), I’ll produce a single internally consistent global placement using the observed rela...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Unchanged (all):

dir: 0.667

facing: 1

pos: 0.636

overall: 0.768

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'basket', 'bike', 'chair', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck', 'wine']

all_changed_objects: ['airplane', 'bike', 'chair', 'wine']

newly_observed_changed (this turn): []

unchanged_objects: ['backpack', 'basket', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck']

pred_changed_keys: []

pred_unchanged_keys: ['airplane', 'backpack', 'basket', 'bike', 'chair', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck', 'wine']

🧭 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': 'chair', 'ori': True}, {'name': 'airplane', 'ori': True}, {'name': 'wine', 'pos': True}, {'name': 'bike', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

🔄 FB Turn 12

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You observe: No objects in field of view..
You have a maximum of 12 exploration steps left.
Strictly follow this format:
THINK:
[Your thoughts on next step actions]
FINAL ANSWER:
Actions: [ ... ]...

🤔 Assistant Thinking (click to toggle)
Continue replicating the original scan pattern from the red doorway. Rotate another 90° clockwise (now facing south) and observe chair/backpack/shoes to check for any of the remaining changes....

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: I will provide a single consistent global grid map (integer coordinates) including the agent, all objects, and both doors, using the initial position as origin and cardinal facings where applicable.
FINAL ANSWER:
{
"agent": { "position": [3, 5], "facing": "south" },
"truck": { "position":...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: chair:

dir: None

pos: None

facing: 1

overall: None

Retention: chair:

dir: None

pos: None

facing: 0

overall: None

Changed: bike:

dir: None

pos: 0.856

facing: None

overall: None

Retention: bike:

dir: None

pos: 0.432

facing: None

overall: None

Unchanged (all):

dir: 0.348

facing: 1

pos: 0.450

overall: 0.599

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'basket', 'bike', 'chair', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck', 'wine']

all_changed_objects: ['airplane', 'bike', 'chair', 'wine']

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

unchanged_objects: ['backpack', 'basket', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck']

pred_changed_keys: ['bike', 'chair']

pred_unchanged_keys: ['airplane', 'backpack', 'basket', 'bike', 'chair', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck', 'wine']

🧭 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': 'chair', 'ori': True}, {'name': 'airplane', 'ori': True}, {'name': 'wine', 'pos': True}, {'name': 'bike', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['chair', 'bike']

newly_observed_unchanged_objects: ['backpack', 'shoes']

🔄 FB Turn 13

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

🤔 Assistant Thinking (click to toggle)
We’ve already detected at least two changes at the red doorway (airplane orientation; chair orientation) and wine is no longer visible there. Next, go to the green door (via a rotation to bring it into FOV) and observe its connected room to check scooter/basket/table and to see whether wine/bike/tru...

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

🗺️ Global Cognitive Map Response (click to toggle)
THINK: Build a single global coordinate map (integers) anchored at the initial agent origin [0,0], using the most recent observations to place all objects/doors and set absolute facings by converting from the agent-relative facings during each observe.
FINAL ANSWER:
{
"agent": { "position": [-3, 1...

📈 Turn Metrics

reward: -0.100

is_done: False

is_valid_action: True

🧭 False Belief Cogmap Metrics

Changed: wine:

dir: None

pos: 0.570

facing: None

overall: None

Retention: wine:

dir: None

pos: 0.216

facing: None

overall: None

Unchanged (all):

dir: 0.803

facing: 1

pos: 0.677

overall: 0.827

🧾 False Belief Object Sets

all_objects: ['airplane', 'backpack', 'basket', 'bike', 'chair', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck', 'wine']

all_changed_objects: ['airplane', 'bike', 'chair', 'wine']

newly_observed_changed (this turn): ['wine']

unchanged_objects: ['backpack', 'basket', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck']

pred_changed_keys: ['wine']

pred_unchanged_keys: ['airplane', 'backpack', 'basket', 'bike', 'chair', 'green door', 'red door', 'scooter', 'shoes', 'shoppingcart', 'table', 'televison', 'truck', 'wine']

🧭 False Belief Info

step: 5

correctly_identified_changes: None

f1_overall: None

f1_position: None

f1_facing: None

action_cost: None

action_cost_after_seen: None

ground_truth_changes: [{'name': 'chair', 'ori': True}, {'name': 'airplane', 'ori': True}, {'name': 'wine', 'pos': True}, {'name': 'bike', 'pos': True}]

reported_changes: []

newly_observed_changed_objects: ['wine']

newly_observed_unchanged_objects: ['scooter', 'table', 'basket']

🔄 FB Turn 14

👤 Environment Observation (click to toggle)
You rotated clockwise 90°. You jumped to green door. You observe:
• scooter: front-right, slightly far, facing backward
• wine: front-right, slightly far
• basket: front-slight-right, mid distance
• table: front-slight-left, mid distance
Assume your current facing direction is called "north":
• sco...

🤔 Assistant Thinking (click to toggle)
Comparing to the original map: (1) airplane’s facing changed (was facing left, now facing forward) while staying in the same general spot; (2) chair’s facing changed (was facing backward, now facing right) from the same viewpoint; (3) wine is no longer by the television/airplane at the red door and ...

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

📈 Turn Metrics

reward: 1

is_done: True

is_valid_action: True

success: 1

🧭 False Belief Info

step: 6

correctly_identified_changes: 1

f1_overall: 1

f1_position: 1

f1_facing: 1

action_cost: 5

action_cost_after_seen: 0

ground_truth_changes: [{'name': 'chair', 'ori': True}, {'name': 'airplane', 'ori': True}, {'name': 'wine', 'pos': True}, {'name': 'bike', 'pos': True}]

reported_changes: [{'name': 'airplane', 'ori': True}, {'name': 'chair', 'ori': True}, {'name': 'wine', 'pos': True}, {'name': 'bike', 'pos': True}]

newly_observed_changed_objects: []

newly_observed_unchanged_objects: []

📊 Task: DirectionEvaluationTask

Question 1 (ID: a2362e4d12a3effe)

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

🤔 Assistant Answer (click to toggle)
THINK: Wine is in the room beyond the red door (to the north of the start), while table is in the room beyond the green door (to the west). So wine should be northeast of table; separated across rooms so likely far.
FINAL ANSWER:
north east, far...

✅ Evaluation Results

id: a2362e4d12a3effe

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

action: None

answer: north east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Evaluation state — Q1: DirectionEvaluationTask

Question 2 (ID: 92c1e7b466fabe79)

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

🤔 Assistant Answer (click to toggle)
THINK:
Televison was northwest of the red door while chair was southwest of the red door, so televison is directly north of chair; separation is roughly the sum of their slightly-far offsets, giving a far distance bin.
FINAL ANSWER:
north, far...

✅ Evaluation Results

id: 92c1e7b466fabe79

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

action: None

answer: north, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 18270900f19204e6)

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

🤔 Assistant Answer (click to toggle)
THINK:
Chair is in room 1 near the red/green door area, while table is seen from the green doorway into the other room, farther to the west and a bit south. So chair should be to the north-east of table, at a moderately large separation.
FINAL ANSWER:
north east, slightly far...

✅ Evaluation Results

id: 18270900f19204e6

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

action: None

answer: east, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

📊 Task: PovEvaluationTask

Question 1 (ID: 3976e12c4008e19b)

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

🤔 Assistant Answer (click to toggle)
THINK: From the earlier joint observation, backpack was slightly to the right and a bit farther than shoes; from shoes while facing shoes’ direction, backpack falls in the front-right sector at about a few meters away.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 3976e12c4008e19b

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

Question 2 (ID: b39109ecaa733d7c)

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

🤔 Assistant Answer (click to toggle)
THINK: From the earlier view, shoes was slightly left of backpack and a bit further along the same forward line; no proximity relation was reported, so separation is likely >2m (mid).
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: b39109ecaa733d7c

question: Now you jump to backpack's direction, facing its direction. Describe where shoes 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: 1c4c6cc481b955ac)

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

🤔 Assistant Answer (click to toggle)
THINK:
At airplane, facing airplane’s direction (west). Television lies west-and-south of airplane (front and left). The separation is a bit over 2 units given both are near to wine but on perpendicular sides, so it bins as mid distance.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: 1c4c6cc481b955ac

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 3c2ff1e4e461e1cf)

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

🤔 Assistant Answer (click to toggle)
THINK:
The only plausible way to see the room-3 objects (scooter, basket) while standing “at an object’s position” is to be at the doorway connecting into that room; the green door is that doorway. Facing the door’s direction would place scooter in front and basket front-left.
FINAL ANSWER:
green do...

✅ Evaluation Results

id: 3c2ff1e4e461e1cf

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

action: None

answer:

answer: bike

final_pos: [6, 5]

final_ori: [-1, 0]

final_observation: [{'name': 'scooter', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'basket', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': None}]

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: default

Question 2 (ID: 697f480eae6ce726)

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

🤔 Assistant Answer (click to toggle)
THINK:
We must be at an object that faces north (so the red door on the north wall appears on the front wall). Shoes would place backpack to the left (since backpack is west of shoes), but the view says backpack is front-right. Chair is west of backpack, so from chair facing north backpack and the r...

✅ Evaluation Results

id: 697f480eae6ce726

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

action: None

answer:

answer: chair

final_pos: [10, 1]

final_ori: [0, 1]

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

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 1

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

Question 3 (ID: a8c816c15da597f7)

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

🤔 Assistant Answer (click to toggle)
THINK:
From the green door view, scooter was positioned northwest of the green door and was facing backward (i.e., toward the green door). If I’m now standing on an object and facing its direction, seeing the green door front-right slightly far and bike straight ahead mid distance is consistent with...

✅ Evaluation Results

id: a8c816c15da597f7

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

action: None

answer:

answer: scooter

final_pos: [2, 5]

final_ori: [1, 0]

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

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: default

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: ead12aceba22dea5)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Identify the referenced objects by the given relative descriptions, track the final position/orientation, then compare shoppingcart’s position relative to that final pose to get its ego direction and distance bin.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: ead12aceba22dea5

question: You return to your starting position and face north. You will execute the following action sequence: 1. Jump to the object at front-right, slightly far. 2. Jump to the object at front-slight-left, slightly far. 3. Rotate(-180) After executing the actions, what is the ego relation of shoppingcart 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

evaluation_mode: default

Question 2 (ID: 329783e3cf710e9c)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Jumping first goes to the truck, then after rotations the mid-distance front-slight-right target is the green door; ending at the green door facing east places the shoes ahead and a bit to the right at a slightly-far distance.
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: 329783e3cf710e9c

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

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 3 (ID: 9778d336cf4718dc)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
After the sequence, I end up at the bike and face south. Basket is diagonally ahead to my right at a mid-range distance.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 9778d336cf4718dc

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front-left, mid distance. 3. Rotate(90) 4. Jump to the object at front-slight-left, mid distance. 5. Rotate(-180) After executing the actions, what is the ego relation of basket relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: d596a6d6b93f9b1f)

❓ 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 left; scooter is at front-left, slightly far, facing right; basket is at front, n...

🤔 Assistant Answer (click to toggle)
THINK: To see bike+scooter+basket together with basket near in front, likely end up at the table (basket is near the table) and face toward the basket. From start, the only immediate jump target in FOV is the red door; from there rotate to bring the green door into FOV, jump to it, then jump to the ...

✅ Evaluation Results

id: d596a6d6b93f9b1f

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

action: None

answer:

final_pos: [4, 1]

final_ori: [0, 1]

room_id: 3

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

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

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

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: False

final_pos: [4.0, 1.0]

final_ori: [1, 0]

visible_match: False

score: 0

evaluation_mode: default

Question 2 (ID: bc2218d7bd5246e5)

❓ 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:
televison is at front-left, mid distance, facing forward; wine is at front, near

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

🤔 Assistant Answer (click to toggle)
THINK:
Need a valid move-only sequence from start (facing north) that ends at a pose where wine is straight ahead (near) and televison is front-left (mid). Use a required initial Rotate, then jump via the red door to the shoppingcart, then rotate to face toward wine.
FINAL ANSWER:
Rotate(0), JumpTo(...

✅ Evaluation Results

id: bc2218d7bd5246e5

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

action: None

answer:

final_pos: [12, 13]

final_ori: [-1, 0]

room_id: 2

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

minimal_plan: [['jumpto', 'red door'], ['jumpto', 'airplane'], ['rotate', -90]]

final_observation: [{'name': 'televison', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'wine', 'direction': 'front', 'distance': 'near', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [14.0, 9.0]

final_ori: [-1, 0]

visible_match: False

score: 0

evaluation_mode: default

Question 3 (ID: 2dd6b807f022852a)

❓ 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 left; wine is at front-left, slightly far

What action sequence led to this fi...

🤔 Assistant Answer (click to toggle)
THINK:
To see airplane and wine in the same relative positions as before, go back to the red door while keeping the initial north-facing orientation; include a non-jump move first to satisfy the “no JumpTo at first step” constraint.
FINAL ANSWER:
Rotate(0), JumpTo(red door)...

✅ Evaluation Results

id: 2dd6b807f022852a

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

action: None

answer:

final_pos: [14, 9]

final_ori: [0, 1]

room_id: 2

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

minimal_plan: [['jumpto', 'red door'], ['jumpto', 'shoppingcart']]

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

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [13.0, 7.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: default

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: ba2ae1056e95b4f4)

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

🤔 Assistant Answer (click to toggle)
THINK:
Use the qualitative bearings/distances from the red doorway to triangulate wine and airplane in the adjacent (north) room, and use the green doorway observation (while facing west) to place the table in the west-connected room; pick integer coordinates consistent with all bins and the stated ...

✅ Evaluation Results

id: ba2ae1056e95b4f4

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

action: None

answer: [[1, 10], [-5, -2], [3, 10]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.900

threshold: 0.900

score: 0.900

evaluation_mode: default

Question 2 (ID: ee801e392523d89c)

❓ 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: televison, chair, shoes, truck, wine.
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 bearings/distances across viewpoints (origin, red door, green door) and the given near/south/east relations among television, wine, airplane.
FINAL ANSWER:
(0, 8); (0, -1); (3, -1); (-2, 3); (0, 9)...

✅ Evaluation Results

id: ee801e392523d89c

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

action: None

answer: [[1, 8], [1, -2], [4, -2], [-1, 2], [1, 10]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.837

threshold: 0.900

score: 0.837

evaluation_mode: default

Question 3 (ID: 4f53c659975c6929)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: truck, shoes, basket.
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/distances: truck at (-1, 2), red door at (4, 4), green door at (-2, -1). Shoes lie directly south of red door at slightly-far distance → (4, -1). Basket is front-slight-right mid-distance from green door while facing wes...

✅ Evaluation Results

id: 4f53c659975c6929

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

action: None

answer: [[-1, 2], [4, -2], [-5, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.851

threshold: 0.900

score: 0.851

evaluation_mode: default

📊 Task: RotEvaluationTask

Question 1 (ID: 8bd34cfbaf041b94)

❓ 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: shoes, shoppingcart, wine.
List them in the...

🤔 Assistant Answer (click to toggle)
THINK: Determine each object’s bearing from the start (0,0) using the relative observations: wine is north of the red door and more westward than the shoppingcart; shoppingcart is north of the red door and more eastward; shoes are south of the red door (thus further clockwise toward east/southeast f...

✅ Evaluation Results

id: 8bd34cfbaf041b94

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

action: None

answer: ['wine', 'shoppingcart', 'shoes']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: a27ba852e9d2d241)

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

🤔 Assistant Answer (click to toggle)
THINK: Determine each object’s approximate bearing from the start (0,0) using earlier doorway-relative observations: shoppingcart is to the north-east (room2), shoes roughly south (room1), chair south-west (room1), table west (room3), televison north-west (room2). Clockwise from north gives the orde...

✅ Evaluation Results

id: a27ba852e9d2d241

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Question 3 (ID: f6d3c113c4f20f24)

❓ 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, chair, shoppingcart, wine.
List t...

🤔 Assistant Answer (click to toggle)
THINK: Using the relative left/right relations observed at the red doorway and the likely room geometry, wine is closest to due north from start, shoppingcart is to the northeast, and backpack then chair are toward the south-to-southwest (chair more westerly than backpack), giving a clockwise order....

✅ Evaluation Results

id: f6d3c113c4f20f24

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

action: None

answer: ['wine', 'shoppingcart', 'backpack', 'chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

📊 Task: RotDualEvaluationTask

Question 1 (ID: 85ffaf62b377f438)

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

B...

🤔 Assistant Answer (click to toggle)
THINK: From the start, the three objects must lie at progressively more clockwise (eastward) bearings to appear in the order wine → shoppingcart → shoes during a continuous 360° sweep, so the rotation direction is clockwise.
FINAL ANSWER:
clockwise...

✅ Evaluation Results

id: 85ffaf62b377f438

question: You return to your starting position and face north. You performed a complete 360° rotation in place. Assume all walls are removed (you can see through walls), so every object is visible. During the rotation, these objects appeared directly in front of you in this order: wine, shoppingcart, shoes 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 2 (ID: c305186d2c81e65b)

❓ 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:
televison, shoppingcart, sho...

🤔 Assistant Answer (click to toggle)
THINK: The sequence matches increasing bearings from north toward east/south/west, which corresponds to a clockwise rotation.
FINAL ANSWER:
clockwise...

✅ Evaluation Results

id: c305186d2c81e65b

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: televison, shoppingcart, shoes, chair, table 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: e727bfe18d95b6fc)

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

🤔 Assistant Answer (click to toggle)
THINK:
Starting facing north, the observed alignment order wine → shoppingcart → backpack → chair matches sweeping the facing direction from north toward east, then south, then west, which corresponds to a clockwise rotation.
FINAL ANSWER:
clockwise...

✅ Evaluation Results

id: e727bfe18d95b6fc

question: You return to your starting position and face north. You performed a complete 360° rotation in place. Assume all walls are removed (you can see through walls), so every object is visible. During the rotation, these objects appeared directly in front of you in this order: wine, shoppingcart, backpack, chair 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: 59a7cc1d9f9a2684)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Truck is directly ahead when facing west; distance falls in the (2,4] bin.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: 59a7cc1d9f9a2684

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

action: None

answer: front, mid distance

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: default

Question 2 (ID: e28473b0be6f4b18)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent is at (0,2) facing south; chair is at (0,1), directly ahead by 1 unit, so it is in front and near.
FINAL ANSWER:
front, near...

✅ Evaluation Results

id: e28473b0be6f4b18

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

action: None

answer: front-slight-left, slightly far

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: default

Question 3 (ID: 6e766b24aeabe349)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Backpack was observed back in room 1, likely southeast of the origin; facing east this places it in the front-right sector at a moderate range.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 6e766b24aeabe349

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

action: None

answer: front, mid distance

task_type: Action2LocationEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: default

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 837814331d62f020)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
From earlier consistent integer placement: truck at (-2, 3) and green door at (-3, 0). Now facing west, “truck at front, mid distance” implies same y and 3 units ahead ⇒ new position (1, 3). This also makes green door front-left at distance 5 (slightly far), matching.
FINAL ANSWER:
(1, 3)...

✅ Evaluation Results

id: 837814331d62f020

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

action: None

answer:

coord: [2, 2]

final_pos: [11, 5]

final_ori: [-1, 0]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.607

raw_score: 0.607

score: 0.607

evaluation_mode: default

Question 2 (ID: f60f38aad8e949fb)

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

🤔 Assistant Answer (click to toggle)
THINK: Use the already-mapped green door location and the new egocentric observation: facing south, the green door is front-right at mid distance, which constrains my position to one of a few integer offsets northeast of the green door. The chair/shoes bearings (both front-left with chair closer to ...

✅ Evaluation Results

id: f60f38aad8e949fb

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

action: None

answer:

coord: [0, 2]

final_pos: [9, 5]

final_ori: [0, -1]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.607

raw_score: 0.607

score: 0.607

evaluation_mode: default

Question 3 (ID: 9bffb5402a1f2564)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: backpack is at front, mid distance, facing right; shoes is at front-right, slightly far, facing left; red door is at front-left, slightly far, on left wall
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?...

🤔 Assistant Answer (click to toggle)
THINK: From the observed bearings/distances in the new pose (facing east), the configuration is consistent with being back at the original origin position.
FINAL ANSWER:
(0, 0)...

✅ Evaluation Results

id: 9bffb5402a1f2564

question: You move to a new location and face east. You observe: backpack is at front, mid distance, facing right; shoes is at front-right, slightly far, facing left; red door is at front-left, slightly far, on left wall Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [0, 0]

final_pos: [9, 3]

final_ori: [1, 0]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: Location2ActionTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: default

📊 Task: DirectionEvaluationTask

Question 1 (ID: a2362e4d12a3effe)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll assign consistent integer coordinates that satisfy all observed egocentric bearings/distances and the reported near-relations (tv–wine, airplane–wine, basket–table, truck–bike). Then I’ll compute wine relative to table from the resulting top-down (global) coordinates.
FINAL ANSWER:
<cogm...

✅ Evaluation Results

id: a2362e4d12a3effe

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

action: None

answer: north east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 92c1e7b466fabe79)

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

🤔 Assistant Answer (click to toggle)
THINK: Assign consistent integer coordinates that satisfy the observed relative bearings/distances and the stated near-relations (tv–wine, airplane–wine, basket–table, truck–bike). Then compute televison relative to chair from the top-down (global) map.
FINAL ANSWER:
<cogmap>
{
"agent": { "positio...

✅ Evaluation Results

id: 92c1e7b466fabe79

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

action: None

answer: north, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 18270900f19204e6)

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

🤔 Assistant Answer (click to toggle)
THINK:
Assign integer coordinates consistent with the observed egocentric bearings/dist-bins across viewpoints (origin, red doorway, green doorway) and the stated near relations. Then compute chair relative to table using the global (top-down) frame.
FINAL ANSWER:
<cogmap>
{
"agent": { "position":...

✅ Evaluation Results

id: 18270900f19204e6

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

action: None

answer: east, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: 3976e12c4008e19b)

❓ Evaluation Question (click to toggle)
Now you jump to shoes'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 provide a globally consistent integer-coordinate map anchored at the initial agent position (0,0), initial facing north. Coordinates are chosen to best satisfy the observed egocentric bearings/distances and the stated proximity relations.
For the question: jumping to shoes and facing t...

✅ Evaluation Results

id: 3976e12c4008e19b

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 2 (ID: b39109ecaa733d7c)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent global integer-coordinate map from the (binned) egocentric observations, anchored at the initial agent pose (0,0) facing north. Use the final exploration pose as the agent position in the map (at the green door, facing north). Then, after hypothetically jumping t...

✅ Evaluation Results

id: b39109ecaa733d7c

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

Question 3 (ID: 1c4c6cc481b955ac)

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

🤔 Assistant Answer (click to toggle)
THINK: Build a globally consistent integer-coordinate map from the qualitative bearings/distances observed at the initial pose and at both doorways; then answer the relative-position query by placing the agent at the airplane and using the airplane’s facing to compute egocentric direction + distance...

✅ Evaluation Results

id: 1c4c6cc481b955ac

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: prompt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 3c2ff1e4e461e1cf)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single global (x,y) map consistent with all egocentric observations and the stated near-relations, keeping integer coordinates. For the final question, the only object whose facing and relative geometry makes scooter appear directly in front at mid distance (and scooter facing bac...

✅ Evaluation Results

id: 3c2ff1e4e461e1cf

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

action: None

answer:

answer: bike

final_pos: [6, 5]

final_ori: [-1, 0]

final_observation: [{'name': 'scooter', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'basket', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': None}]

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: 697f480eae6ce726)

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

🤔 Assistant Answer (click to toggle)
THINK: Build a single consistent global coordinate assignment (integers) that satisfies all observed egocentric bearings/distances and the given proximity relations; then test each candidate “stand on object, face its facing” against the described view. The configuration matches best when standing a...

✅ Evaluation Results

id: 697f480eae6ce726

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

action: None

answer:

answer: chair

final_pos: [10, 1]

final_ori: [0, 1]

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

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 1

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: a8c816c15da597f7)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single consistent global map (integer coordinates) from the sequence of egocentric observations and door-jumps. Use the initial agent pose as origin (0,0), facing north. Place doors/objects so that all observed relative bearings/distances and the stated near-relations are simultan...

✅ Evaluation Results

id: a8c816c15da597f7

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

action: None

answer:

answer: scooter

final_pos: [2, 5]

final_ori: [1, 0]

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

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: ead12aceba22dea5)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Build a single consistent integer-coordinate map that satisfies all qualitative observations (bearings/distances/proximities) without using Query(). Then simulate the given action sequence from the start pose (0,0) facing north: identify the unique front-right slightly-far object (red door), ...

✅ Evaluation Results

id: ead12aceba22dea5

question: You return to your starting position and face north. You will execute the following action sequence: 1. Jump to the object at front-right, slightly far. 2. Jump to the object at front-slight-left, slightly far. 3. Rotate(-180) After executing the actions, what is the ego relation of shoppingcart 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: 329783e3cf710e9c)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
I need a single consistent global (x,y) assignment (integers) matching all the egocentric observations and the few stated proximity relations, then simulate the given action sequence from the start to compute shoes’ final ego relation. I’ll use the last known agent pose at termination (at the...

✅ Evaluation Results

id: 329783e3cf710e9c

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

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 9778d336cf4718dc)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
I need an integer-coordinate global map consistent with the qualitative observations. Since no Query() was used, I choose one consistent placement that satisfies all reported relative bearings/distances and near-relations across viewpoints.
Then, using that map, I simulate the given action se...

✅ Evaluation Results

id: 9778d336cf4718dc

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front-left, mid distance. 3. Rotate(90) 4. Jump to the object at front-slight-left, mid distance. 5. Rotate(-180) After executing the actions, what is the ego relation of basket relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: d596a6d6b93f9b1f)

❓ 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 left; scooter is at front-left, slightly far, facing right; basket is at front, n...

🤔 Assistant Answer (click to toggle)
THINK:
I’ll construct a single consistent global coordinate assignment (integer grid) that matches the observed relative bearings/distances and the given close-object relations. Then, for the second part, I’ll find a move-only sequence starting from (0,0) facing north that ends at a pose whose FOV w...

✅ Evaluation Results

id: d596a6d6b93f9b1f

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

action: None

answer:

final_pos: [4, 1]

final_ori: [0, 1]

room_id: 3

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

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

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

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [4.0, 1.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: bc2218d7bd5246e5)

❓ 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:
televison is at front-left, mid distance, facing forward; wine is at front, near

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

🤔 Assistant Answer (click to toggle)
THINK: Build a single consistent global coordinate assignment for all observed entities (using integer positions consistent with the qualitative bearings/distances and stated near-relations). Then, for the final-view question, pick a previously observed jump target and rotation that would make wine ...

✅ Evaluation Results

id: bc2218d7bd5246e5

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

action: None

answer:

final_pos: [12, 13]

final_ori: [-1, 0]

room_id: 2

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

minimal_plan: [['jumpto', 'red door'], ['jumpto', 'airplane'], ['rotate', -90]]

final_observation: [{'name': 'televison', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'wine', 'direction': 'front', 'distance': 'near', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [12.0, 13.0]

final_ori: [-1, 0]

visible_match: True

score: 1

evaluation_mode: prompt_cogmap

Question 3 (ID: 2dd6b807f022852a)

❓ 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 left; wine is at front-left, slightly far

What action sequence led to this fi...

🤔 Assistant Answer (click to toggle)
THINK:
Construct a globally consistent integer-coordinate map from the relative observations (using the initial pose as origin and cardinal facings). For the final-view question, the simplest way to see airplane and wine front-left slightly-far is to be at the red door facing north; since a JumpTo c...

✅ Evaluation Results

id: 2dd6b807f022852a

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

action: None

answer:

final_pos: [14, 9]

final_ori: [0, 1]

room_id: 2

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

minimal_plan: [['jumpto', 'red door'], ['jumpto', 'shoppingcart']]

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

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [13.0, 7.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: prompt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: ba2ae1056e95b4f4)

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

🤔 Assistant Answer (click to toggle)
THINK:
I must output a single global cognitive map in JSON with integer coordinates, using the start as origin (0,0) and north as +y. I’ll place all observed items/doors on a consistent grid that satisfies the observed relative bearings/distances and stated near-relations (e.g., airplane east of win...

✅ Evaluation Results

id: ba2ae1056e95b4f4

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

action: None

answer: [[1, 10], [-5, -2], [3, 10]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.903

threshold: 0.900

score: 0.903

evaluation_mode: prompt_cogmap

Question 2 (ID: ee801e392523d89c)

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

🤔 Assistant Answer (click to toggle)
THINK: Build a consistent approximate global coordinate assignment (integer grid) from all relative observations/jumps, anchored at start (0,0) facing north; then read off requested object coordinates in order.
FINAL ANSWER:
<cogmap>
```json
{
"agent": { "position": [-3, 1], "facing": "north" },

...

✅ Evaluation Results

id: ee801e392523d89c

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

action: None

answer: [[1, 8], [1, -2], [4, -2], [-1, 2], [1, 10]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.661

threshold: 0.900

score: 0.661

evaluation_mode: prompt_cogmap

Question 3 (ID: 4f53c659975c6929)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: truck, shoes, basket.
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 produce a single consistent global map in integer (x,y) with start at (0,0) facing north. Since no Query() actions were used, exact coordinates are underdetermined; I’ll provide one coherent integer layout consistent with all observed relative bearings/distances and proximity constrain...

✅ Evaluation Results

id: 4f53c659975c6929

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

action: None

answer: [[-1, 2], [4, -2], [-5, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.744

threshold: 0.900

score: 0.744

evaluation_mode: prompt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 8bd34cfbaf041b94)

❓ 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: shoes, shoppingcart, wine.
List them in the...

🤔 Assistant Answer (click to toggle)
THINK: Build a single consistent integer-coordinate map (approximate due to binned distances/bearings) containing all observed items/doors relative to the start. Then compute each target object’s bearing from the start and order them by when they align with the forward direction during a clockwise 3...

✅ Evaluation Results

id: 8bd34cfbaf041b94

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

action: None

answer: ['wine', 'shoppingcart', 'shoes']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

Question 2 (ID: a27ba852e9d2d241)

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

🤔 Assistant Answer (click to toggle)
THINK:
Construct a single globally consistent integer-coordinate layout that satisfies the observed relative bearings/distances and the reported near-relations, then compute each target object’s clockwise bearing from the start to order them during a 360° rotation.

FINAL ANSWER:
<cogmap>
```json
{
...

✅ Evaluation Results

id: a27ba852e9d2d241

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 3 (ID: f6d3c113c4f20f24)

❓ 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, chair, shoppingcart, wine.
List t...

🤔 Assistant Answer (click to toggle)
THINK:
I need integer coordinates in a single global frame with the start as (0,0), north as +y. Since no Query() was used, I’ll construct one consistent coordinate assignment that matches all relative/FOV constraints and the given near-relations. Then I’ll compute each target object’s bearing from ...

✅ Evaluation Results

id: f6d3c113c4f20f24

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

action: None

answer: ['wine', 'shoppingcart', 'backpack', 'chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: prompt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 59a7cc1d9f9a2684)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Assign consistent integer coordinates (relative to the initial origin) that satisfy the observed egocentric bearings/distances and the stated proximity relations. Then compute the truck’s egocentric direction/distance from the new pose: agent at (2,2) facing west.
FINAL ANSWER:
<cogmap>
```js...

✅ Evaluation Results

id: 59a7cc1d9f9a2684

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

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 2 (ID: e28473b0be6f4b18)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
I will construct one consistent integer-coordinate global map that satisfies all observed egocentric/cardinal relations and distance bins (without any Query data, exact coordinates are underdetermined, so I choose a simplest consistent placement). Then from the hypothetical new pose (0,2) fac...

✅ Evaluation Results

id: e28473b0be6f4b18

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

action: None

answer: front-slight-left, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

Question 3 (ID: 6e766b24aeabe349)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Infer a consistent integer-coordinate map from the sequence of egocentric observations at (0,0), red door, and green door (including the final rotate at green door). Then compute backpack’s egocentric direction/distance from (0,0) when facing east: backpack at (1,1) is 45° to the left of the ...

✅ Evaluation Results

id: 6e766b24aeabe349

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

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: prompt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 837814331d62f020)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll build a single consistent global integer-coordinate map using the initial pose as (0,0) facing north, fitting all observed relative bearings/distances and the noted proximity constraints (tv south of wine; airplane east of wine; basket east of table in the local frame at the green door; ...

✅ Evaluation Results

id: 837814331d62f020

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

action: None

answer:

coord: [2, 2]

final_pos: [11, 5]

final_ori: [-1, 0]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.607

raw_score: 0.607

score: 0.607

evaluation_mode: prompt_cogmap

Question 2 (ID: f60f38aad8e949fb)

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

🤔 Assistant Answer (click to toggle)
THINK:
I’ll provide a single consistent global JSON map (integer coordinates) for all observed objects/doors plus the agent. Then, using the stated new observation while facing south, I triangulate the new agent position relative to the already-mapped green door, with chair/shoes serving as a consis...

✅ Evaluation Results

id: f60f38aad8e949fb

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

action: None

answer:

coord: [0, 2]

final_pos: [9, 5]

final_ori: [0, -1]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.243

raw_score: 0.243

score: 0.243

evaluation_mode: prompt_cogmap

Question 3 (ID: 9bffb5402a1f2564)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: backpack is at front, mid distance, facing right; shoes is at front-right, slightly far, facing left; red door is at front-left, slightly far, on left wall
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?...

🤔 Assistant Answer (click to toggle)
THINK: I must output a global cognitive map with integer coordinates (origin at the agent’s initial (0,0), facing north). Since no Query() actions were used, exact coordinates aren’t uniquely determined by the binned observations; I provide one consistent integer realization. Then, using that map (r...

✅ Evaluation Results

id: 9bffb5402a1f2564

question: You move to a new location and face east. You observe: backpack is at front, mid distance, facing right; shoes is at front-right, slightly far, facing left; red door is at front-left, slightly far, on left wall Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [0, 0]

final_pos: [9, 3]

final_ori: [1, 0]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0

raw_score: 0

score: 0

evaluation_mode: prompt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: a2362e4d12a3effe)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute wine relative to table using their coordinates, then bin into cardinal direction and distance.
FINAL ANSWER:
north-east, far...

✅ Evaluation Results

id: a2362e4d12a3effe

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

action: None

answer: north east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 92c1e7b466fabe79)

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

🤔 Assistant Answer (click to toggle)
THINK:
Televison (1,8) relative to chair (1,-2) is straight north by Euclidean distance 10, which bins to far.
FINAL ANSWER:
north, far...

✅ Evaluation Results

id: 92c1e7b466fabe79

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

action: None

answer: north, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 18270900f19204e6)

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

🤔 Assistant Answer (click to toggle)
THINK:
Chair (1,-2) is 6 units east of table (-5,-2), so direction is east and distance bin is slightly far.
FINAL ANSWER:
east, slightly far...

✅ Evaluation Results

id: 18270900f19204e6

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

action: None

answer: east, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: 3976e12c4008e19b)

❓ Evaluation Question (click to toggle)
Now you jump to shoes'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 am at shoes (4,-2) facing north; backpack is at (3,0), which is 1 left and 2 forward from me (front-left) at distance √5 ≈ 2.24 (mid distance).
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: 3976e12c4008e19b

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: b39109ecaa733d7c)

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent is at backpack (3,0) facing south. Shoes at (4,-2) is 2 units forward (south) and 1 unit left (east relative to facing south), giving a front-left bearing. Distance sqrt(5) falls in (2,4] → mid distance.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: b39109ecaa733d7c

question: Now you jump to backpack's direction, facing its direction. Describe where shoes 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: 1c4c6cc481b955ac)

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

🤔 Assistant Answer (click to toggle)
THINK:
At airplane position (3,10) facing west, televison at (1,8) is 2 left and 2 behind in global terms, which is front-left in the airplane-facing frame; distance √8 ≈ 2.83 → mid distance.
FINAL ANSWER:
front-left, mid distance...

✅ Evaluation Results

id: 1c4c6cc481b955ac

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 3c2ff1e4e461e1cf)

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent must be at an object whose facing makes scooter appear directly in front at mid distance and facing backward. Standing at the bike (facing west), scooter lies 4 units straight ahead (west) and faces east (backward). Basket is front-left at mid distance.
FINAL ANSWER:
bike...

✅ Evaluation Results

id: 3c2ff1e4e461e1cf

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

action: None

answer:

answer: bike

final_pos: [6, 5]

final_ori: [-1, 0]

final_observation: [{'name': 'scooter', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'basket', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': None}]

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: 697f480eae6ce726)

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

🤔 Assistant Answer (click to toggle)
THINK:
Backpack is observed “facing backward”, and in the map backpack faces south, so the agent must be facing north. The candidate north-facing objects are chair, shoes, and red door. From chair (1,-2) facing north: truck at (-1,2) falls at bearing ≈ -26.6° (front-left) and distance ≈ 4.47 (slight...

✅ Evaluation Results

id: 697f480eae6ce726

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

action: None

answer:

answer: chair

final_pos: [10, 1]

final_ori: [0, 1]

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

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 1

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: a8c816c15da597f7)

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

🤔 Assistant Answer (click to toggle)
THINK:
Bike being directly in front at mid distance matches standing at scooter (facing east): scooter→bike is (4,0). Basket and green door also fall in the front-right sector at the given distances.
FINAL ANSWER:
scooter...

✅ Evaluation Results

id: a8c816c15da597f7

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

action: None

answer:

answer: scooter

final_pos: [2, 5]

final_ori: [1, 0]

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

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: ead12aceba22dea5)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
From start (0,0) facing north, “front-right, slightly far” is the red door at (4,4). From there, “front-slight-left, slightly far” is the airplane at (3,10). After Rotate(-180), I face south at (3,10). Shoppingcart at (5,6) is at vector (2,-4), which is ahead-left when facing south, at distan...

✅ Evaluation Results

id: ead12aceba22dea5

question: You return to your starting position and face north. You will execute the following action sequence: 1. Jump to the object at front-right, slightly far. 2. Jump to the object at front-slight-left, slightly far. 3. Rotate(-180) After executing the actions, what is the ego relation of shoppingcart 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: 329783e3cf710e9c)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Track pose through the given actions, identify the jumped-to objects by the described ego bins, then compute shoes’ ego bearing/distance from the final pose.
FINAL ANSWER:
front-slight-right, slightly far...

✅ Evaluation Results

id: 329783e3cf710e9c

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

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 9778d336cf4718dc)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute final agent pose after the rotations/jumps, then compute basket’s bearing/distance in the agent’s egocentric frame (facing south).
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 9778d336cf4718dc

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front-left, mid distance. 3. Rotate(90) 4. Jump to the object at front-slight-left, mid distance. 5. Rotate(-180) After executing the actions, what is the ego relation of basket relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: d596a6d6b93f9b1f)

❓ 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 left; scooter is at front-left, slightly far, facing right; basket is at front, n...

🤔 Assistant Answer (click to toggle)
THINK:
We need to end at (-5,-2) facing north so basket is front near and bike/scooter are front-right/front-left slightly far. From start, rotate to bring the target jump object (table at -5,-2) into FOV, jump to it, then rotate back to north.
FINAL ANSWER:
Rotate(-90), JumpTo(table), Rotate(90)...

✅ Evaluation Results

id: d596a6d6b93f9b1f

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

action: None

answer:

final_pos: [4, 1]

final_ori: [0, 1]

room_id: 3

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

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

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

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [4.0, 1.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: bc2218d7bd5246e5)

❓ 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:
televison is at front-left, mid distance, facing forward; wine is at front, near

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

🤔 Assistant Answer (click to toggle)
THINK:
We need a valid move-only sequence from start (0,0) facing north to a pose where wine is directly in front within 2 units, and televison is front-left at mid distance facing forward. This matches being at airplane’s position (3,10) and facing west. Since JumpTo can’t be the first action, star...

✅ Evaluation Results

id: bc2218d7bd5246e5

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

action: None

answer:

final_pos: [12, 13]

final_ori: [-1, 0]

room_id: 2

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

minimal_plan: [['jumpto', 'red door'], ['jumpto', 'airplane'], ['rotate', -90]]

final_observation: [{'name': 'televison', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'wine', 'direction': 'front', 'distance': 'near', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [12.0, 13.0]

final_ori: [-1, 0]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 2dd6b807f022852a)

❓ 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 left; wine is at front-left, slightly far

What action sequence led to this fi...

🤔 Assistant Answer (click to toggle)
THINK: To see only airplane and wine front-left, the agent can end at the shoppingcart facing north. From the start, the shoppingcart isn’t in FOV (different room), so jump to the red door first (visible), then jump to the shoppingcart (visible from the doorway). Add an initial Rotate(0) to satisfy ...

✅ Evaluation Results

id: 2dd6b807f022852a

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

action: None

answer:

final_pos: [14, 9]

final_ori: [0, 1]

room_id: 2

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

minimal_plan: [['jumpto', 'red door'], ['jumpto', 'shoppingcart']]

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

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: True

ori_match: True

final_pos: [14.0, 9.0]

final_ori: [0, 1]

visible_match: True

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: ba2ae1056e95b4f4)

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

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided reference cognitive map coordinates for the requested objects in the specified order.
FINAL ANSWER:
(1, 10); (-5, -2); (3, 10)...

✅ Evaluation Results

id: ba2ae1056e95b4f4

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

action: None

answer: [[1, 10], [-5, -2], [3, 10]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: ee801e392523d89c)

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

🤔 Assistant Answer (click to toggle)
THINK:
Use the provided ground-truth cognitive map and output allocentric (x, y) coordinates in the requested object order.
FINAL ANSWER:
(1, 8); (1, -2); (4, -2); (-1, 2); (1, 10)...

✅ Evaluation Results

id: ee801e392523d89c

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

action: None

answer: [[1, 8], [1, -2], [4, -2], [-1, 2], [1, 10]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 4f53c659975c6929)

❓ Evaluation Question (click to toggle)
Treat your starting position as the origin (0, 0) while facing north.
Report allocentric coordinates using (x right/east, y up/north).
Objects: truck, shoes, basket.
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 relative to the agent’s start at (0,0), in the requested object order.
FINAL ANSWER:
(-1, 2); (4, -2); (-5, 0)...

✅ Evaluation Results

id: 4f53c659975c6929

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

action: None

answer: [[-1, 2], [4, -2], [-5, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

threshold: 0.900

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 8bd34cfbaf041b94)

❓ 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: shoes, shoppingcart, wine.
List them in the...

🤔 Assistant Answer (click to toggle)
THINK:
Compute clockwise bearings from the agent at (0,0) facing north (0°): wine ≈ 5.7°, shoppingcart ≈ 39.8°, shoes ≈ 116.6°. Order is increasing bearing during clockwise rotation.
FINAL ANSWER:
wine, shoppingcart, shoes...

✅ Evaluation Results

id: 8bd34cfbaf041b94

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

action: None

answer: ['wine', 'shoppingcart', 'shoes']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: a27ba852e9d2d241)

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

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s clockwise bearing from the agent at (0,0) facing north (bearing = atan2(x,y)), then sort ascending from 0° to 360°.
FINAL ANSWER:
televison, shoppingcart, shoes, chair, table...

✅ Evaluation Results

id: a27ba852e9d2d241

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: f6d3c113c4f20f24)

❓ 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, chair, shoppingcart, wine.
List t...

🤔 Assistant Answer (click to toggle)
THINK:
Compute each object’s clockwise bearing from north at the origin (angle = atan2(x, y) in degrees) and sort ascending over a 0–360° clockwise rotation.
FINAL ANSWER:
wine, shoppingcart, backpack, chair...

✅ Evaluation Results

id: f6d3c113c4f20f24

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

action: None

answer: ['wine', 'shoppingcart', 'backpack', 'chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 59a7cc1d9f9a2684)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (2,2) facing west; truck at (-1,2) is 3 units straight ahead (along forward direction). Distance 3 falls in (2,4] → mid distance.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: 59a7cc1d9f9a2684

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

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: e28473b0be6f4b18)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (0,2) facing south; chair at (1,-2). Relative vector (1,-4) is mostly forward (south) with slight left offset; distance ≈4.12 → slightly far.
FINAL ANSWER:
front-slight-left, slightly far...

✅ Evaluation Results

id: e28473b0be6f4b18

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

action: None

answer: front-slight-left, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 6e766b24aeabe349)

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

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

🤔 Assistant Answer (click to toggle)
THINK:
Agent at (0,0) facing east; backpack at (3,0) lies straight ahead (0°) at distance 3, which is mid distance.
FINAL ANSWER:
front, mid distance...

✅ Evaluation Results

id: 6e766b24aeabe349

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

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_gt_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 837814331d62f020)

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

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

🤔 Assistant Answer (click to toggle)
THINK: Facing west, “truck at front” implies the truck is directly west of me with the same y. Truck is at (-1, 2), so my y is 2 and my x is > -1. “Mid distance” gives |x - (-1)| in (2,4], so x ∈ {2,3}. Using green door (-2,-1) being front-left and slightly far is consistent; pick x=2.
FINAL ANSWER:...

✅ Evaluation Results

id: 837814331d62f020

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

action: None

answer:

coord: [2, 2]

final_pos: [11, 5]

final_ori: [-1, 0]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

Question 2 (ID: f60f38aad8e949fb)

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

🤔 Assistant Answer (click to toggle)
THINK:
Match the observed egocentric bearings/distances (facing south) to known object coordinates; (0, 2) yields chair front-slight-left slightly-far, shoes front-left slightly-far, and green door front-right mid-distance.
FINAL ANSWER:
(0, 2)...

✅ Evaluation Results

id: f60f38aad8e949fb

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

action: None

answer:

coord: [0, 2]

final_pos: [9, 5]

final_ori: [0, -1]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 1

raw_score: 1

score: 1

evaluation_mode: use_gt_cogmap

Question 3 (ID: 9bffb5402a1f2564)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: backpack is at front, mid distance, facing right; shoes is at front-right, slightly far, facing left; red door is at front-left, slightly far, on left wall
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?...

🤔 Assistant Answer (click to toggle)
THINK:
Backpack at (3,0) is directly in front while facing east, so agent must share y=0 and be west of x=3 by a mid (2–4) distance. Testing x=-1 fits: backpack distance 4 (mid), shoes at (4,-2) becomes front-right and slightly far, and red door at (4,4) becomes front-left and slightly far.
FINAL AN...

✅ Evaluation Results

id: 9bffb5402a1f2564

question: You move to a new location and face east. You observe: backpack is at front, mid distance, facing right; shoes is at front-right, slightly far, facing left; red door is at front-left, slightly far, on left wall Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [0, 0]

final_pos: [9, 3]

final_ori: [1, 0]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0

raw_score: 0

score: 0

evaluation_mode: use_gt_cogmap

📊 Task: DirectionEvaluationTask

Question 1 (ID: a2362e4d12a3effe)

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

🤔 Assistant Answer (click to toggle)
THINK: From table (-5,-1) to wine (1,10) is +6 east and +11 north, which is north-east; Euclidean distance √157 ≈ 12.5, which bins to far.
FINAL ANSWER:
north-east, far...

✅ Evaluation Results

id: a2362e4d12a3effe

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

action: None

answer: north east, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 92c1e7b466fabe79)

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

🤔 Assistant Answer (click to toggle)
THINK: Using the cognitive map coordinates: chair at [-2,-1], televison at [1,9]. From chair to televison is Δx=+3 (east), Δy=+10 (north) giving a near-north direction and distance ≈10.4 which bins to far.
FINAL ANSWER:
north, far...

✅ Evaluation Results

id: 92c1e7b466fabe79

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

action: None

answer: north, far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 18270900f19204e6)

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

🤔 Assistant Answer (click to toggle)
THINK: From table (-5,-1) to chair (-2,-1) is +3 in x and 0 in y, so chair is due east at distance 3 (mid distance bin).
FINAL ANSWER:
east, mid distance...

✅ Evaluation Results

id: 18270900f19204e6

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

action: None

answer: east, slightly far

task_type: DirectionEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

📊 Task: PovEvaluationTask

Question 1 (ID: 3976e12c4008e19b)

❓ Evaluation Question (click to toggle)
Now you jump to shoes'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 shoes, you are at (3,-2) facing north; backpack at (1,-2) lies directly left (west) at distance 2, which is outside the ±45° egocentric bins, so it is beyond the forward FOV; distance bin is near.
FINAL ANSWER:
beyond-fov, near...

✅ Evaluation Results

id: 3976e12c4008e19b

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: b39109ecaa733d7c)

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

🤔 Assistant Answer (click to toggle)
THINK: After jumping to backpack at (1,-2) and facing south, shoes at (3,-2) are 2 units to the agent’s left (90° off forward), which is outside the ±45° egocentric bins; distance is 2 → near.
FINAL ANSWER:
beyond-fov, near...

✅ Evaluation Results

id: b39109ecaa733d7c

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: 1c4c6cc481b955ac)

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

🤔 Assistant Answer (click to toggle)
THINK: After jumping to airplane at (2,10) and facing west, televison at (1,9) is 1 unit forward and 1 unit left (bearing -45°), at distance √2 ≈ 1.41 (near).
FINAL ANSWER:
front-left, near...

✅ Evaluation Results

id: 1c4c6cc481b955ac

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

action: None

answer: front-left, mid distance

task_type: PovEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 3c2ff1e4e461e1cf)

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

🤔 Assistant Answer (click to toggle)
THINK: From the bike (facing west), the scooter lies exactly straight ahead at distance 4 (mid) and faces east (backward relative to us); the basket is ahead-left at mid distance. This matches the observation.
FINAL ANSWER:
bike...

✅ Evaluation Results

id: 3c2ff1e4e461e1cf

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

action: None

answer:

answer: bike

final_pos: [6, 5]

final_ori: [-1, 0]

final_observation: [{'name': 'scooter', 'direction': 'front', 'distance': 'mid distance', 'orientation': 'facing backward'}, {'name': 'basket', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': None}]

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: 697f480eae6ce726)

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

🤔 Assistant Answer (click to toggle)
THINK: The viewpoint must be from an object that faces north (since truck is front-left and red door/backpack are front-right, matching the initial north-facing frame). Chair’s facing is consistent with this and its likely location southwest of the red door makes the red door and backpack appear fro...

✅ Evaluation Results

id: 697f480eae6ce726

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

action: None

answer:

answer: chair

final_pos: [10, 1]

final_ori: [0, 1]

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

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 1

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: True

view_match: True

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: a8c816c15da597f7)

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

🤔 Assistant Answer (click to toggle)
THINK: Since after the jump I face the object’s own facing direction, and I see the bike “front” while the bike is “facing backward” relative to me, I must be facing east while the bike faces west. The observed object that faces east is the truck (it was “facing right”). This also fits bike being ne...

✅ Evaluation Results

id: a8c816c15da597f7

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

action: None

answer:

answer: scooter

final_pos: [2, 5]

final_ori: [1, 0]

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

object_positions:

truck: [8.0, 5.0]

televison: [10.0, 11.0]

bike: [6.0, 5.0]

chair: [10.0, 1.0]

airplane: [12.0, 13.0]

scooter: [2.0, 5.0]

backpack: [12.0, 3.0]

shoes: [13.0, 1.0]

wine: [10.0, 13.0]

basket: [4.0, 3.0]

shoppingcart: [14.0, 9.0]

table: [4.0, 1.0]

green door: [7.0, 2.0]

red door: [13.0, 7.0]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

room_id: 3

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: BackwardPovTextEvaluationTask

choices: []

kwargs:

(none)

name_match: False

view_match: False

score: 0

evaluation_mode: use_model_cogmap

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: ead12aceba22dea5)

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

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

🤔 Assistant Answer (click to toggle)
THINK: From start, the front-right slightly far object is the red door; from the red door (facing north), the front-slight-left slightly far object is the airplane. After rotating -180 at the airplane, the shoppingcart—previously front-right mid from the red door—lies generally ahead-left of the new...

✅ Evaluation Results

id: ead12aceba22dea5

question: You return to your starting position and face north. You will execute the following action sequence: 1. Jump to the object at front-right, slightly far. 2. Jump to the object at front-slight-left, slightly far. 3. Rotate(-180) After executing the actions, what is the ego relation of shoppingcart 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_model_cogmap

Question 2 (ID: 329783e3cf710e9c)

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

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

🤔 Assistant Answer (click to toggle)
THINK: From start, front-left mid is truck → jump to truck (face north). Rotate -180 → face south. From truck facing south, the front-slight-right mid object is green door → jump there. Rotate -90 → face east. From green door facing east, shoes are ahead and slightly to the right at ~5.4 units.
FINA...

✅ Evaluation Results

id: 329783e3cf710e9c

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

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: 9778d336cf4718dc)

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

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

🤔 Assistant Answer (click to toggle)
THINK: After rotations/jumps, I end at the bike facing south; basket is 2 units west and 2 units south of me, which is front-right in the south-facing frame, at mid distance.
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: 9778d336cf4718dc

question: You return to your starting position and face north. You will execute the following action sequence: 1. Rotate(-90) 2. Jump to the object at front-left, mid distance. 3. Rotate(90) 4. Jump to the object at front-slight-left, mid distance. 5. Rotate(-180) After executing the actions, what is the ego relation of basket relative to you? Answer format: <ego direction>, <distance> Example: front, near

action: None

answer: front-right, mid distance

task_type: Action2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: d596a6d6b93f9b1f)

❓ 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 left; scooter is at front-left, slightly far, facing right; basket is at front, n...

🤔 Assistant Answer (click to toggle)
THINK: End view matches being at the table facing north (basket 2m ahead; bike/scooter ~4.5m ahead-right/left). From start, use the red door (visible), rotate to bring green door into FOV, jump there, rotate to see table, then jump to table.
FINAL ANSWER:
JumpTo(red door), Rotate(-90), JumpTo(green ...

✅ Evaluation Results

id: d596a6d6b93f9b1f

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

action: None

answer:

final_pos: [4, 1]

final_ori: [0, 1]

room_id: 3

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

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

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

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

error: target_not_visible

score: 0

evaluation_mode: use_model_cogmap

Question 2 (ID: bc2218d7bd5246e5)

❓ 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:
televison is at front-left, mid distance, facing forward; wine is at front, near

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

🤔 Assistant Answer (click to toggle)
THINK: To see wine close ahead and the television ahead-left, move to the red door first, then to the chair (seen from the red door when facing south), then face back north toward room 2.
FINAL ANSWER:
Rotate(0), JumpTo(red door), Rotate(180), JumpTo(chair), Rotate(180)...

✅ Evaluation Results

id: bc2218d7bd5246e5

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

action: None

answer:

final_pos: [12, 13]

final_ori: [-1, 0]

room_id: 2

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

minimal_plan: [['jumpto', 'red door'], ['jumpto', 'airplane'], ['rotate', -90]]

final_observation: [{'name': 'televison', 'direction': 'front-left', 'distance': 'mid distance', 'orientation': 'facing forward'}, {'name': 'wine', 'direction': 'front', 'distance': 'near', 'orientation': None}]

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: False

final_pos: [10.0, 1.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: 2dd6b807f022852a)

❓ 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 left; wine is at front-left, slightly far

What action sequence led to this fi...

🤔 Assistant Answer (click to toggle)
THINK: The final view matches being positioned at the red door while facing north; since JumpTo cannot be the first action, do a zero-rotation first, then jump to the red door (which is in initial FOV).
FINAL ANSWER:
Rotate(0), JumpTo(red door)...

✅ Evaluation Results

id: 2dd6b807f022852a

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

action: None

answer:

final_pos: [14, 9]

final_ori: [0, 1]

room_id: 2

init_pos: [9, 3]

init_ori: [0, 1]

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

minimal_plan: [['jumpto', 'red door'], ['jumpto', 'shoppingcart']]

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

task_type: View2ActionTextEvaluationTask

choices: []

kwargs:

(none)

pos_match: False

ori_match: True

final_pos: [13.0, 7.0]

final_ori: [0, 1]

visible_match: False

score: 0

evaluation_mode: use_model_cogmap

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: ba2ae1056e95b4f4)

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

🤔 Assistant Answer (click to toggle)
THINK: Use the previously constructed global cognitive map coordinates (origin at initial agent position, x=east, y=north) and report the requested objects in the given order.
FINAL ANSWER:
(1, 10); (-5, -1); (2, 10)...

✅ Evaluation Results

id: ba2ae1056e95b4f4

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

action: None

answer: [[1, 10], [-5, -2], [3, 10]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.956

threshold: 0.900

score: 0.956

evaluation_mode: use_model_cogmap

Question 2 (ID: ee801e392523d89c)

❓ 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: televison, chair, shoes, truck, wine.
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 (origin at start) to read off allocentric (x,y) for the requested objects in the given order.
FINAL ANSWER:
(1, 9); (-2, -1); (3, -2); (-1, 3); (1, 10)...

✅ Evaluation Results

id: ee801e392523d89c

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

action: None

answer: [[1, 8], [1, -2], [4, -2], [-1, 2], [1, 10]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.786

threshold: 0.900

score: 0.786

evaluation_mode: use_model_cogmap

Question 3 (ID: 4f53c659975c6929)

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

🤔 Assistant Answer (click to toggle)
THINK: Use the previously constructed global cognitive map with start as (0,0) and allocentric axes (x east, y north); extract coordinates for truck, shoes, basket in order.
FINAL ANSWER:
(-1, 3); (3, -2); (-5, 1)...

✅ Evaluation Results

id: 4f53c659975c6929

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

action: None

answer: [[-1, 2], [4, -2], [-5, 0]]

task_type: AlloMappingEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.808

threshold: 0.900

score: 0.808

evaluation_mode: use_model_cogmap

📊 Task: RotEvaluationTask

Question 1 (ID: 8bd34cfbaf041b94)

❓ 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: shoes, shoppingcart, wine.
List them in the...

🤔 Assistant Answer (click to toggle)
THINK: Compute clockwise bearings from start (0,0) facing north: wine ≈ 5.7°, shoppingcart ≈ 35.5°, shoes ≈ 123.7°; order is increasing bearing during clockwise rotation.
FINAL ANSWER:
wine, shoppingcart, shoes...

✅ Evaluation Results

id: 8bd34cfbaf041b94

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

action: None

answer: ['wine', 'shoppingcart', 'shoes']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 2 (ID: a27ba852e9d2d241)

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

🤔 Assistant Answer (click to toggle)
THINK: Use inferred global coordinates relative to origin to compute each target’s clockwise bearing from north; sort by bearing (ties by nearer distance).
FINAL ANSWER:
televison, shoppingcart, shoes, chair, table...

✅ Evaluation Results

id: a27ba852e9d2d241

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

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

Question 3 (ID: f6d3c113c4f20f24)

❓ 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, chair, shoppingcart, wine.
List t...

🤔 Assistant Answer (click to toggle)
THINK: Compute each target object’s clockwise bearing from the start (0,0) facing north, then sort by bearing in [0,360).
FINAL ANSWER:
wine, shoppingcart, backpack, chair...

✅ Evaluation Results

id: f6d3c113c4f20f24

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

action: None

answer: ['wine', 'shoppingcart', 'backpack', 'chair']

task_type: RotEvaluationTask

choices: []

kwargs:

(none)

score: 1

evaluation_mode: use_model_cogmap

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 59a7cc1d9f9a2684)

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

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

🤔 Assistant Answer (click to toggle)
THINK: From (2,2) facing west, truck at (-1,3) is 3 units forward and 1 unit to the right (bearing ≈ +18° → front-slight-right); distance √10 ≈ 3.16 → mid distance.
FINAL ANSWER:
front-slight-right, mid distance...

✅ Evaluation Results

id: 59a7cc1d9f9a2684

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

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

Question 2 (ID: e28473b0be6f4b18)

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

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

🤔 Assistant Answer (click to toggle)
THINK: From agent at (0,2) facing south, chair at (-2,-1) is 3 units forward and 2 units to the right (west), giving a front-right bearing (~34°) and distance √13 ≈ 3.6 (mid).
FINAL ANSWER:
front-right, mid distance...

✅ Evaluation Results

id: e28473b0be6f4b18

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

action: None

answer: front-slight-left, slightly far

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0

evaluation_mode: use_model_cogmap

Question 3 (ID: 6e766b24aeabe349)

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

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

🤔 Assistant Answer (click to toggle)
THINK: From (0,0) facing east, backpack at (1,-2) is 63.4° to the right of forward (outside ±45°), with distance √5 ≈ 2.24 (mid distance).
FINAL ANSWER:
beyond-fov, mid distance...

✅ Evaluation Results

id: 6e766b24aeabe349

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

action: None

answer: front, mid distance

task_type: Location2ViewEvaluationTask

choices: []

kwargs:

(none)

score: 0.500

evaluation_mode: use_model_cogmap

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 837814331d62f020)

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

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

🤔 Assistant Answer (click to toggle)
THINK: Facing west means “front” is global west. So the truck being at front implies the new position is directly east of the truck (same y). Using the known truck and green door global positions from the built map, the only integer position that keeps truck at mid distance and green door at front-l...

✅ Evaluation Results

id: 837814331d62f020

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

action: None

answer:

coord: [2, 2]

final_pos: [11, 5]

final_ori: [-1, 0]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.702

raw_score: 0.702

score: 0.702

evaluation_mode: use_model_cogmap

Question 2 (ID: f60f38aad8e949fb)

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

🤔 Assistant Answer (click to toggle)
THINK: Use the known global positions of chair, shoes, and the green door from the built map and triangulate the only integer agent position consistent with their observed bearings (facing south) and distance bins; this yields (-1, 3).
FINAL ANSWER:
(-1, 3)...

✅ Evaluation Results

id: f60f38aad8e949fb

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

action: None

answer:

coord: [0, 2]

final_pos: [9, 5]

final_ori: [0, -1]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0.493

raw_score: 0.493

score: 0.493

evaluation_mode: use_model_cogmap

Question 3 (ID: 9bffb5402a1f2564)

❓ Evaluation Question (click to toggle)
You move to a new location and face east.
You observe: backpack is at front, mid distance, facing right; shoes is at front-right, slightly far, facing left; red door is at front-left, slightly far, on left wall
Still treat your initial position as origin (0, 0)
What is your new 2D coordinate (x, y)?...

🤔 Assistant Answer (click to toggle)
THINK: Use known global coordinates of red door (3,5), backpack (2,1), and shoes (3,-2). With agent facing east, “front” means +x, so backpack must be directly east of the agent at mid distance (3). That places agent at (-1,1). Check: shoes from (-1,1) are (4,-3) → front-right slightly far; red door...

✅ Evaluation Results

id: 9bffb5402a1f2564

question: You move to a new location and face east. You observe: backpack is at front, mid distance, facing right; shoes is at front-right, slightly far, facing left; red door is at front-left, slightly far, on left wall Still treat your initial position as origin (0, 0) What is your new 2D coordinate (x, y)? Answer format: (x, y) Example: (2, -1)

action: None

answer:

coord: [0, 0]

final_pos: [9, 3]

final_ori: [1, 0]

room_id: 1

object_positions:

truck: [8, 5]

televison: [10, 11]

bike: [6, 5]

chair: [10, 1]

airplane: [12, 13]

scooter: [2, 5]

backpack: [12, 3]

shoes: [13, 1]

wine: [10, 13]

basket: [4, 3]

shoppingcart: [14, 9]

table: [4, 1]

green door: [7, 2]

red door: [13, 7]

object_orientations:

truck: [1, 0]

televison: [-1, 0]

bike: [-1, 0]

chair: [0, 1]

airplane: [-1, 0]

scooter: [1, 0]

backpack: [0, -1]

shoes: [0, 1]

shoppingcart: [0, -1]

green door: [1, 0]

red door: [0, 1]

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

gate_info:

green door:

room_ids: [3, 1]

ori_by_room:

3: [-1, 0]

1: [1, 0]

red door:

room_ids: [1, 2]

ori_by_room:

1: [0, -1]

2: [0, 1]

object_rooms:

truck: 1

televison: 2

bike: 3

chair: 1

airplane: 2

scooter: 3

backpack: 1

shoes: 1

wine: 2

basket: 3

shoppingcart: 2

table: 3

green door: [3, 1]

red door: [1, 2]

task_type: View2LocationTextEvaluationTask

choices: []

kwargs:

(none)

similarity: 0

raw_score: 0

score: 0

evaluation_mode: use_model_cogmap

Model: gpt-5.2

sample_run01

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

🧭 False Belief Exploration

🔄 FB Turn 9

🔄 FB Turn 10

🔄 FB Turn 11

🔄 FB Turn 12

🔄 FB Turn 13

🔄 FB Turn 14

📊 Evaluation (default)

📊 Task: DirectionEvaluationTask

Question 1 (ID: a2362e4d12a3effe)

Question 2 (ID: 92c1e7b466fabe79)

Question 3 (ID: 18270900f19204e6)

📊 Task: PovEvaluationTask

Question 1 (ID: 3976e12c4008e19b)

Question 2 (ID: b39109ecaa733d7c)

Question 3 (ID: 1c4c6cc481b955ac)

📊 Task: BackwardPovTextEvaluationTask

Question 1 (ID: 3c2ff1e4e461e1cf)

Question 2 (ID: 697f480eae6ce726)

Question 3 (ID: a8c816c15da597f7)

📊 Task: Action2ViewEvaluationTask

Question 1 (ID: ead12aceba22dea5)

Question 2 (ID: 329783e3cf710e9c)

Question 3 (ID: 9778d336cf4718dc)

📊 Task: View2ActionTextEvaluationTask

Question 1 (ID: d596a6d6b93f9b1f)

Question 2 (ID: bc2218d7bd5246e5)

Question 3 (ID: 2dd6b807f022852a)

📊 Task: AlloMappingEvaluationTask

Question 1 (ID: ba2ae1056e95b4f4)

Question 2 (ID: ee801e392523d89c)

Question 3 (ID: 4f53c659975c6929)

📊 Task: RotEvaluationTask

Question 1 (ID: 8bd34cfbaf041b94)

Question 2 (ID: a27ba852e9d2d241)

Question 3 (ID: f6d3c113c4f20f24)

📊 Task: RotDualEvaluationTask

Question 1 (ID: 85ffaf62b377f438)

Question 2 (ID: c305186d2c81e65b)

Question 3 (ID: e727bfe18d95b6fc)

📊 Task: Location2ViewEvaluationTask

Question 1 (ID: 59a7cc1d9f9a2684)

Question 2 (ID: e28473b0be6f4b18)

Question 3 (ID: 6e766b24aeabe349)

📊 Task: View2LocationTextEvaluationTask

Question 1 (ID: 837814331d62f020)

Question 2 (ID: f60f38aad8e949fb)

Question 3 (ID: 9bffb5402a1f2564)