LLM Misinterprets Wind Direction in Real-World Spatial Contexts (Flat 2D Vector Reasoning vs. True Spatial Awareness)

[Zemmix12]

Title: LLM Misinterprets Wind Direction in Real-World Spatial Contexts (Flat 2D Vector Reasoning vs. True Spatial Awareness)

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Summary:
Large language models like GPT-4 consistently fail to reason correctly about airflow direction relative to window orientation and user position in physical space. The problem is not a misunderstanding of labels like "north-facing," but a deeper limitation: the model reduces real-world airflow to flat 2D vector alignment and lacks any 3D spatial awareness or physical modeling.

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🔍 Problem Description:

When prompted with:

“My window faces north. A southerly wind is blowing. I'm indoors, facing the window.”

The correct human interpretation is:

The wind is blowing from behind the person (south), toward the window (north).

But GPT-4 repeatedly states that:

“The wind is coming from the front,”
as if the “south-to-north” vector automatically aligns with the direction the window faces.

This is not because the model misreads “north-facing” as “wind coming from the north.”
The real issue is that the model applies 2D vector logic only, assuming:

wind vector (south → north) + window vector (north-facing) = perfect alignment = wind hits directly.

It completely ignores:

• Where the person is positioned
• That a window is on the north wall, not floating in open space
• That airflow needs to pass through the interior spatial environment

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🧱 Core Limitation:

• No understanding of building geometry, room layout, or object placement
• No concept of observer point-of-view
• No spatial alignment between wind origin, person, and window
• Treats “wind direction” and “window direction” as abstract 2D arrows, not physical vectors in a 3D world

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✅ Example of what the model misses:

A “south wind” means air moves from south to north.
If a person is standing indoors facing a north-facing window, that window is on the north wall, and the wind is approaching the person from behind (from the south).

LLMs don’t understand this. They simply align “north-facing” with “wind toward north” and falsely conclude that the wind hits the user’s face.

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🧠 Suggested Direction:

• Integrate a spatial frame of reference model tied to indoor/outdoor context
• Use scene graphs, CAD/BIM layouts, or geometry grounding
• Couple LLMs with 3D simulation modules or hybrid reasoning systems
• Incorporate examples that reflect observer perspective + structural orientation

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Severity: High
This limitation affects not only physical reasoning but the model’s reliability in any environment where geometry, flow, or orientation matter (e.g., smart homes, robotics, architecture, HVAC).

Tags:
#LLM_Limitation #Spatial_Reasoning #Airflow_Misunderstanding #3D_Context_Missing