ICL-Surg: In-Context Learning with Logic Refinement for Surgical Phase Recognition

Few-shot surgical phase recognition aims to segment surgical videos into semantically meaningful phases using only a few annotated samples. However, the scarcity of labeled data limits existing methods in capturing the temporal structure and discriminative patterns of surgical workflows. To address these challenges,we present ICL-Surg, a novel framework that combines in-context learning with workflow-guided logic refinement. Given a single annotated demonstration video,ICL-Surg extracts complementary local temporal and global scene representations and adaptively fuses them via a Dynamic Confidence-aware Fusion (DCF) mechanism, which balances local and global similarities according to modality confidence. For each segment in a query video, phase labels are assigned by weighted cosine similarity to the demonstration. To enforce temporal coherence and medical plausibility, we present a Workflow-guided Logic Refinement (WLR) module that applies Viterbi decoding with dual-source priors: a local transition matrix derived from the demonstration, and empirical transition and positional statistics aggregated from a label pool, combined by linear interpolation. This module explicitly refines the predicted phase sequence against surgical logic. Extensive experiments across multiple public surgical video benchmarks demonstrate that ICL-Surg outperforms existing few-shot surgical phase recognition methods.Code will be released upon acceptance.