Conan: Progressive Learning to Reason Like a Detective over Multi-Scale
  Visual Evidence
Authors: name: Kun Ouyang, name: Yuanxin Liu, name: Linli Yao, name: Yishuo Cai, name: Hao Zhou, name: Jie Zhou, name: Fandong Meng, name: Xu Sun
Organization: Peking University

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.