Thinking with Images via Self-Calling Agent
Thinking-with-images paradigms have showcased remarkable visual reasoning capability by integrating visual information as dynamic elements into the Chain-of-Thought (CoT). However, optimizing interleaved multimodal CoT (iMCoT) through reinforcement learning remains challenging, as it relies on scarce high-quality reasoning data. In this study, we propose Self-Calling Chain-of-Thought (sCoT), a novel visual reasoning paradigm that reformulates iMCoT as a language-only CoT with self-calling. Specifically, a main agent decomposes the complex visual reasoning task to atomic subtasks and invokes its virtual replicas, i.e. parameter-sharing subagents, to solve them in isolated context. sCoT enjoys substantial training effectiveness and efficiency, as it requires no explicit interleaving between modalities. sCoT employs group-relative policy optimization to reinforce effective reasoning behavior to enhance optimization. Experiments on HR-Bench 4K show that sCoT improves the overall reasoning performance by up to 1.9% with sim 75% fewer GPU hours compared to strong baseline approaches. Code is available at https://github.com/YWenxi/think-with-images-through-self-calling.
693bcd629874a2a5e4ffb41a
Wenxi Yang
claimed_verified
2025-12-12T09:14:15.266Z
662f1aa0e4a1f7302a7f134a
/avatars/2fef164e2ccd24c7f3c46942e4b12ab5.svg
Wenxi Yang
ywenxi
user
ywenxi
693bcd629874a2a5e4ffb41b
Yuzhong Zhao
claimed_verified
2025-12-12T09:14:13.057Z
635b6b3590204c509f9755bd
/avatars/94398dd17ca1a2e74dc8d775abf636a7.svg
zhaoyuzhong
callsys
user
callsys
693bcd629874a2a5e4ffb41c
Fang Wan
693bcd629874a2a5e4ffb41d
Qixiang Ye
Thinking with Images via Self-Calling Agent
Thinking-with-images paradigms have showcased remarkable visual reasoning capability by integrating visual information as dynamic elements into the Chain-of-Thought (CoT). However, optimizing interleaved multimodal CoT (iMCoT) through reinforcement learning remains challenging, as it relies on scarce high-quality reasoning data. In this study, we propose Self-Calling Chain-of-Thought (sCoT), a novel visual reasoning paradigm that reformulates iMCoT as a language-only CoT with self-calling. Specifically, a main agent decomposes the complex visual reasoning task to atomic subtasks and invokes its virtual replicas, i.e. parameter-sharing subagents, to solve them in isolated context. sCoT enjoys substantial training effectiveness and efficiency, as it requires no explicit interleaving between modalities. sCoT employs group-relative policy optimization to reinforce effective reasoning behavior to enhance optimization. Experiments on HR-Bench 4K show that sCoT improves the overall reasoning performance by up to 1.9% with sim 75% fewer GPU hours compared to strong baseline approaches. Code is available at https://github.com/YWenxi/think-with-images-through-self-calling.
sCoT, a language-only CoT paradigm with self-calling subagents, enhances visual reasoning performance and efficiency through group-relative policy optimization.
Chain-of-Thought CoT interleaved multimodal CoT iMCoT reinforcement learning visual reasoning parameter-sharing subagents group-relative policy optimization HR-Bench 4K