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
Published: 2025-10-23 12:11:46

Abstract: 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.