Position, Not Provenance: Separating Reasoning Mediation from Sycophancy in Medical Vision-Language Models

This paper investigates whether the chain-of-thought (CoT) reasoning generated by medical vision-language models (VLMs) actually drives their final clinical decisions or if it is merely decorative. To distinguish between genuine reasoning mediation and sycophancy (where a model simply agrees with authoritative-looking text), the authors developed CoT-Mediate. This framework perturbs specific clinical attributes—such as laterality, severity, or diagnosis—within the model's own reasoning and observes if the final answer shifts to align with the edited information.

The researchers employed a dual-arm protocol to test two injection methods: re-prompting (presenting the edit as external evidence) and prefix-forced continuation (inserting the edit directly into the model's own reasoning sequence). They also conducted a provenance sweep, holding the edited text constant while varying the attributed source (e.g., self, senior radiologist, or medical student) to isolate the impact of perceived authority on model behavior.

The study reveals that the injection mechanism significantly dictates the faithfulness of the model. Prefix-forced continuation consistently resulted in higher mediation faithfulness than re-prompting, suggesting that re-prompting provides only a lower bound on how much a model actually uses its reasoning.

Regarding provenance, the models exhibited distinct behaviors: LLaVA-Med was largely authority-blind but showed a specific aversion to its own self-attributed text, while MedGemma displayed a clear authority gradient. Notably, both models showed increased reliance on textual reasoning when visual evidence was ablated, and laterality was consistently the most difficult clinical attribute for the models to track faithfully. The findings suggest that the models are more sensitive to the structural position of the reasoning in their context window than to the stated source of that reasoning.

Understanding whether medical VLMs are truly reasoning or merely performing sycophantic pattern matching is critical for clinical safety. If a model's reasoning is not load-bearing, clinicians cannot rely on it as an explanation for the model's diagnosis. This research provides a rigorous methodology for auditing these models, demonstrating that current methods of evaluating faithfulness are highly sensitive to how the reasoning is presented, which has significant implications for the development of trustworthy AI in healthcare.

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