SurgCoT: Advancing Spatiotemporal Reasoning in Surgical Videos through a Chain-of-Thought Benchmark

• URL: http://arxiv.org/abs/2604.20319v1
• Date: Wed, 22 Apr 2026 08:18:21 GMT
• Title: SurgCoT: Advancing Spatiotemporal Reasoning in Surgical Videos through a Chain-of-Thought Benchmark
• Abstract summary: SurgTCo is a benchmark for evaluating chain-of-thought (CoT) reasoning in MLLMs across 7 specialties and 35 diverse procedures.<n>SurgCoT assesses five core reasoning dimensions: Causal Action Ordering Cue-Action Alignment, Affordance Mapping, Micro-Transition, and Cluelyset Tracking.
• Score: 48.24206502894029
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-grained spatiotemporal reasoning on surgical videos is critical, yet the capabilities of Multi-modal Large Language Models (MLLMs) in this domain remain largely unexplored. To bridge this gap, we introduce SurgCoT, a unified benchmark for evaluating chain-of-thought (CoT) reasoning in MLLMs across 7 surgical specialties and 35 diverse procedures. SurgCoT assesses five core reasoning dimensions: Causal Action Ordering, Cue-Action Alignment, Affordance Mapping, Micro-Transition Localization, and Anomaly Onset Tracking, through a structured CoT framework with an intensive annotation protocol (Question-Option-Knowledge-Clue-Answer), where the Knowledge field provides essential background context and Clue provides definitive spatiotemporal evidence. Evaluation of 10 leading MLLMs shows: 1) commercial models outperform open-source and medical-specialized variants; 2) significant gaps exist in surgical CoT reasoning; 3) SurgCoT enables effective evaluation and enhances progressive spatiotemporal reasoning. SurgCoT provides a reproducible testbed to narrow the gap between MLLM capabilities and clinical reasoning demands. Code: https://github.com/CVI-SZU/SurgCoT.

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