EviAgent: Evidence-Gain-Guided Active Evidence Acquisition for Long-Horizon Surgical Video Reasoning

Long-horizon surgical video reasoning remains challenging because query-relevant evidence is often sparse, heterogeneous, and dynamically dependent on what has already been established. Existing long-video methods improve context coverage through compression, retrieval, or adaptive observation, but the relationship between established evidence, unresolved evidence, and the next acquisition decision often remains implicit. We therefore formulate long-horizon surgical video reasoning as active evidence acquisition and propose EviAgent, an evidence-seeking agent that explicitly targets unresolved, query-dependent evidence in long surgical videos. EviAgent maintains a structured evidence state and uses an evidence-gain-guided policy to adaptively select temporal retrieval, local verification, or stopping as reasoning progresses. We evaluate EviAgent on four surgical subsets of MedHorizon under procedure-level cross-fitted evaluation, covering both fine-grained surgical understanding and semantic reasoning. Anonymous code is available at: https://anonymous.4open.science/r/eviagent-CC46.