Conan: Progressive Learning to Reason Like a Detective over Multi-Scale
  Visual Evidence
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.
68faec7af158a71c5a2f5879
Kun Ouyang
68faec7af158a71c5a2f587a
Yuanxin Liu
68faec7af158a71c5a2f587b
Linli Yao
68faec7af158a71c5a2f587c
Yishuo Cai
68faec7af158a71c5a2f587d
Hao Zhou
68faec7af158a71c5a2f587e
Jie Zhou
68faec7af158a71c5a2f587f
Fandong Meng
68faec7af158a71c5a2f5880
Xu Sun
Conan: Progressive Learning to Reason Like a Detective over Multi-Scale
  Visual Evidence
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.
Conan, a framework for evidence-grounded multi-step video reasoning, enhances visual grounding and reasoning accuracy through a multi-stage training strategy and outperforms existing models on various benchmarks.
video reasoning multimodal large language models reinforcement learning frame-retrieval contextual frames evidence frames cross-frame clues multi-stage progressive cold-start strategy Identification-Reasoning-Action (AIR) RLVR training framework long-video understanding