Do LLMs Build World Models From Text? A Multilingual Diagnostic of Spatial Reasoning D
AI benchmark · Spatial Reasoning
- Publisher
- Paper Arxiv
- Domains
- Spatial Reasoning
- Tier
- D why
- Snapshot
What is Do LLMs Build World Models From Text? A Multilingual Diagnostic of Spatial Reasoning?
Whether large language models (LLMs) construct internal spatial world models from pure-text descriptions remains contested, and whether such capabilities transfer across languages has not been systematically studied. We introduce MentalMap, a multilingual diagnostic benchmark with a six-level capability hierarchy (L0-L5) spanning atomic spatial facts to generative world-graph construction, together with four diagnostic axes probing frame of reference, reading-direction bias, reasoning-effort allocation, and hallucination. MentalMap is built from 100 ProcTHOR household scenes, covers eight typologically diverse languages plus a structured-text control, and contains 39 task families across 1,950 evaluation cells. Evaluating thirteen LLMs across scales and model families, we identify a universal L3 reasoning cliff: no model retains even half of its L0 performance on viewpoint reasoning once baseline atomic accuracy exceeds 40%. The cliff persists across languages, scales, and prompting strategies, while structured-output failures and reasoning patterns vary substantially across models. Human evaluation under the identical pure-text protocol reproduces the same failure pattern, suggesting that the bottleneck arises from text-only working memory constraints rather than being specific to current LLM architectures. Our findings reframe pure-text spatial reasoning as a multi-axis world-modeling problem and motivate multimodal and scratchpad-augmented reasoning as future directions.
Tier D
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- 13 models scored
- no proper name (a paper title or description)
- in curated RL directories (1 pt)
How we got to tier D
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- In curated RL directories: 1 point, a floor of none.
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