Conan: Progressive Learning to Reason Like a Detective over Multi-Scale Visual Evidence

Kun Ouyang, Yuanxin Liu, Linli Yao, yi, Hao Zhou, Jie Zhou, Fandong Meng, Xu Sun

Conan: Progressive Learning to Reason Like a Detective over Multi-Scale Visual Evidence: 12 upvotes on Hugging Face Daily Papers, #14 of 28 papers on 2025-10-24. Day-by-day upvote history.

Video reasoning, which requires multi-step deduction across frames, remains a major challenge for multimodal large language models (MLLMs). While reinforcement learning (RL)-based methods enhance reasoning capabilities, they often rely on text-only chains that yield ungrounded or hallucinated conclusions. Conversely, frame-retrieval approaches introduce visual grounding but still struggle with inaccurate evidence localization. To address these challenges, we present Conan, a framework for evidence-grounded multi-step video reasoning. Conan identifies contextual and evidence frames, reasons over cross-frame clues, and adaptively decides when to conclude or explore further. To achieve this, we (1) construct Conan-91K, a large-scale dataset of automatically generated reasoning traces that includes frame identification, evidence reasoning, and action decision, and (2) design a multi-stage progressive cold-start strategy combined with an Identification-Reasoning-Action (AIR) RLVR training framework to jointly enhance multi-step visual reasoning. Extensive experiments on six multi-step reasoning benchmarks demonstrate that Conan surpasses the baseline Qwen2.5-VL-7B-Instruct by an average of over 10% in accuracy, achieving state-of-the-art performance. Furthermore, Conan generalizes effectively to long-video understanding tasks, validating its strong scalability and robustness.

Paper page on Hugging Face · arXiv

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