ClinCoT: Clinical-Aware Visual Chain-of-Thought for Medical Vision Language Models

Large vision-language models (VLMs) often benefit from chain-of-thought (CoT) prompting in general domains, yet its efficacy in medical vision-language tasks remains underexplored. We report a counter-intuitive trend: on medical visual question answering, CoT frequently underperforms direct answering (DirA) across general-purpose and medical-specific models. We attribute this to a medical perception bottleneck: subtle, domain-specific cues can weaken visual grounding, and CoT may compound early perceptual uncertainty rather than correct it. To probe this hypothesis, we introduce two training-free, inference-time grounding interventions: (i) perception anchoring via region-of-interest cues and (ii) description grounding via high-quality textual guidance. Across multiple benchmarks and model families, these interventions improve accuracy, mitigate CoT degradation, and in several settings reverse the CoT--DirA inversion. Our findings suggest that reliable clinical VLMs require robust visual grounding and cross-modal alignment, beyond extending text-driven reasoning chains. Code is available here.

Preference optimization is increasingly used to post-train medical large vision-language models (LVLMs), yet it operates at a much coarser granularity than the one that defines clinical correctness. Whether one response is clinically better than another usually comes down to a few decisive phrases, such as an anatomical laterality or a lesion attribute, and to whether each is supported by the image region the question concerns. Direct Preference Optimization (DPO) and its variants, by contrast, reduce the comparison to a single response-level scalar, an objective structurally unable to represent which tokens carry clinical meaning. We show that this mismatch is costly: of the objectives we compare, response-level DPO places the least reward on the phrases that decide clinical correctness, and substituting supervised references for preferred responses opens a stylistic gap that the model exploits as a reward-hacking shortcut, raising preference accuracy but not clinical accuracy. To capture the full feedback in a clinical comparison, we propose Fine-grained Regularized Medical Preference Optimization (FiRe-MPO). Preference pairs are built by minimally editing the model's own generations, so that preferred and rejected responses differ only on clinically decisive spans, and each is paired with a lesion-corrupted image withholding the supporting visual evidence. Because span-localized rewards are sparse, we stabilize optimization with a bidirectional token-wise KL regularizer. Across medical visual question answering and report generation, FiRe-MPO outperforms DPO, RRPO, and competing fine-grained objectives on two popular LVLMs, one medical and one general-purpose, while strengthening visual grounding and placing more reward on the medical phrases than the alternatives.

Vision-Language Models (VLMs) have enabled interpretable medical diagnosis by integrating visual perception with linguistic reasoning. Yet, existing medical chain-of-thought (CoT) models lack explicit mechanisms to represent and enforce causal reasoning, leaving them vulnerable to spurious correlations and limiting their clinical reliability. We pinpoint three core challenges in medical CoT reasoning: how to adaptively trigger causal correction, construct high-quality causal-spurious contrastive samples, and maintain causal consistency across reasoning trajectories. To address these challenges, we propose MedCausalX, an end-to-end framework explicitly models causal reasoning chains in medical VLMs. We first introduce the CRMed dataset providing fine-grained anatomical annotations, structured causal reasoning chains, and counterfactual variants that guide the learning of causal relationships beyond superficial correlations. Building upon CRMed, MedCausalX employs a two-stage adaptive reflection architecture equipped with $\langle$causal$\rangle$ and $\langle$verify$\rangle$ tokens, enabling the model to autonomously determine when and how to perform causal analysis and verification. Finally, a trajectory-level causal correction objective optimized through error-attributed reinforcement learning refines the reasoning chain, allowing the model to distinguish genuine causal dependencies from shortcut associations. Extensive experiments on multiple benchmarks show that MedCausalX consistently outperforms state-of-the-art methods, improving diagnostic consistency by +5.4 points, reducing hallucination by over 10 points, and attaining top spatial grounding IoU, thereby setting a new standard for causally grounded medical reasoning. The code and dataset are available at https://github.com/zhcz328/MedCausalX.