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.

Multimodal Large Language Models (MLLMs) exhibit strong generalization and reasoning abilities due to large-scale multimodal pre-training. However, fine-tuning these models on downstream tasks often leads to catastrophic forgetting, where newly learned task-specific knowledge degrades previously acquired capabilities. This issue arises because gradient updates for new tasks overwrite parameters critical to prior knowledge, limiting the practical deployment of MLLMs. To address this challenge, we propose **Activation-Weighted Adaptive REtention (AWARe)**, a fine-tuning method that mitigates catastrophic forgetting by dynamically controlling parameter updates based on activation patterns. AWARe assigns activation-based importance scores to parameters, selectively freezing those essential for preserving prior capabilities while allowing less important parameters to adapt to new tasks. Importantly, AWARe operates without modifying model architectures, ensuring compatibility with existing inference engines. Extensive experiments demonstrate that AWARe effectively preserves upstream capabilities while achieving superior downstream performance compared to existing methods. Code is available at https://github.com/kaln27/AWARe.