Medical vision-language models (VLMs) are often evaluated by report retrieval, grounding, and explanation quality. We show that these metrics can create a metric illusion: a model may appear clinically grounded while exploiting acquisition template, crop, orientation, or protocol rather than patient anatomy. We formalize this failure as template-induced ambiguity and introduce TIA-VL, a counterfactual benchmark with source, template-family, severity, and compound protocol shifts. We also propose Rationale-Grounded Template Ambiguity Calibration (R-GTAC), a plug-in calibration layer that discounts template-dominated evidence while preserving anatomy and textual rationale support. Across strong medical VLM-style baselines, including BioViL-T-style frozen features, adapter features, and anatomy-teacher features, R-GTAC reduces wrong same-template errors to 0.0000 on the main protocol splits. We then test whether systems know when to abstain through readiness and counterfactual rationale deferral. Counterfactual rationale readiness filtering further identifies a reliable low-risk 25% subset reaching 0.8500 Top-1 on the hardest compound shift, while revealing that coupled protocol shifts pose critical frontiers for global risk calibration. Thus, TIA-VL supports a conservative message for medical VLM evaluation: models should be judged not only by whether they answer, but by whether their apparent rationales survive anti-shortcut counterfactuals. The code and benchmark will be available at https://github.com/YifanLuo0103/TIA-VL.

Vision-Language Models (VLMs) show immense promise in medicine, yet surgical scene understanding remains formidable due to complex visual obstructions and rigorous multi-step clinical logic. Standard Supervised Fine-Tuning (SFT) imparts domain knowledge, but we identify a critical flaw—the "Reasoning Collapse" phenomenon: during answer-only SFT, models progressively lose explicit step-by-step reasoning capabilities, regressing into "black boxes" that generate uninterpretable shortcut answers—a severe risk for clinical traceability. Constructing high-quality Chain-of-Thought (CoT) datasets to mitigate this is prohibitively expensive in the surgical domain. We propose SurgMind-R1, a surgical VLM with a three-stage training pipeline. We construct Cholec-Think, a reasoning-enhanced dataset combining expert and VLM-assisted CoT annotations; apply domain-adaptive SFT and reasoning-enhanced SFT on Cholec-Think to internalize surgical logic; and introduce Reinforcement Learning via Group Relative Policy Optimization (GRPO) with rule-based rewards to enforce structured thinking paths and eliminate reasoning collapse. On SurgLite-Bench, our comprehensive benchmark, SurgMind-R1 significantly outperforms state-of-the-art VLMs on complex inference tasks. It internalizes structured thinking, maintaining high accuracy in a direct no-think mode that resolves the high-latency bottleneck of reasoning models for real-time clinical applications, and exhibits strong zero-shot out-of-distribution (OOD) generalization across unseen surgical scenarios and novel tasks, paving the way for transparent, trustworthy computer-assisted interventions. The code, benchmark, and models will be available at https://github.com/YifanLuo0103/SurgMind-R1.