Effective human teams excel at maintaining a consistent shared mental model (SMM) that reflects the shared understanding of individual team members about the task and what remains to be done. We present a novel, two-step framework that leverages large language models (LLMs) both as (1) annotators of team dialogues to track the team’s SMM and (2) as automated discrepancy detectors among individuals’ mental states as they are represented in individual mental models. We define an SMM coherence evaluation framework for this use case and apply it to six dialogues in a previously published team corpus, ultimately producing a dataset of human and LLM SMM annotations, a reproducible evaluation framework for SMM coherence, and an empirical assessment of LLM-based discrepancy detection. Our results reveal that while LLMs exhibit apparent coherence on straightforward natural-language annotation tasks, they systematically err in scenarios requiring spatial reasoning or disambiguation of transcription-level disfluencies.

How do LLMs learn in-context? Is it by pattern-matching recent tokens, or by inferring latent structure? We probe this question using a toy graph random-walk across two competing graph structures. We present behavioral, representational, and causal evidence that neither account alone is sufficient. First, a complexity-weighted sigmoid model captures topology-dependent evidence accumulation, and PCA shows that both graph topologies are visually recoverable at intermediate mixture ratios. Second, residual-stream activation patching and graph-difference steering causally intervene on this graph-family signal: late-layer patching almost fully transfers the clean graph preference, while linear steering moves predictions in the intended direction and fails under norm-matched and label-shuffled controls. An explicit transition-cache baseline reproduces the fitted asymmetry and held-out-edge transfer, however, so these results do not by themselves distinguish global inference from local copying. Taken together, our findings are consistent with a dual-mechanism account in which structure inference and induction circuits operate in parallel, but do not uniquely identify it.