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
claimed_verified
2025-10-27T10:29:37.616Z
6489761dcaea79f577897f98
/avatars/8f56dc9c08dc2b672555602d68509a03.svg
Yuanxin Liu
lyx97
user
lyx97
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