World Models in Words: Auditing Physical State-Transition Commitments in Vision-Language Models

Vision-language models (VLMs) are increasingly used to answer questions about physical scenes, yet most evaluations reduce performance to a final answer. This hides whether the model perceived the right objects, represented the right physical state, predicted a plausible transition, or merely selected the right option for the wrong reasons. We introduce wmw, an evaluation framework for auditing the language-expressed physical commitments of VLMs. Instead of scoring only I,qmapsto a, we ask models to produce a typed trace I,qmapsto(s0,Delta s,s1,a): an initial state, a state transition, a resulting state, and an answer. A hybrid verifier then checks schema validity, state grounding, transition consistency, and answer-trace compatibility, yielding typed error labels such as object, relation, force, transition, temporal, unit/scale, and faithfulness errors. We release tracebank, a controlled trace resource with nSeed schema- and recomputation-validated synthetic scenarios across nFamilies physics families, nPairs minimally perturbed contrastive preference pairs, verifier code, audit guidelines, and model outputs. We evaluate nModels VLMs on both controlled and external physical-reasoning examples. wmw reveals failures that answer-only evaluation misses: 35% of correct answers from mid-tier models are backed by physically invalid traces. Verifier-guided reranking recovers up to 7 percentage points of trace validity without sacrificing answer accuracy, and trace-level preference tuning reduces hidden inconsistency by 41% relative. The contribution is not another final-answer physics benchmark, but a reusable protocol for measuring whether a VLM's stated physical world can be true at the same time as its answer.