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arXiv · 2510.24698

ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking

Baixuan Li·Dingchu Zhang·Jialong Wu·Wenbiao Yin·Zhengwei Tao·Yida Zhao·Liwen Zhang·Haiyang Shen·Runnan Fang·Pengjun Xie·Jingren Zhou·Yong Jiang
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Abstract

Parallel thinking expands exploration breadth, complementing the deep exploration of information-seeking (IS) agents to further enhance problem-solving capability. However, conventional parallel thinking faces two key challenges in this setting: inefficiency from repeatedly rolling out from scratch, and difficulty in integrating long-horizon reasoning trajectories during answer generation, as limited context capacity prevents full consideration of the reasoning process. To address these issues, we propose ParallelMuse, a two-stage paradigm designed for deep IS agents. The first stage, Functionality-Specified Partial Rollout, partitions generated sequences into functional regions and performs uncertainty-guided path reuse and branching to enhance exploration efficiency. The second stage, Compressed Reasoning Aggregation, exploits reasoning redundancy to losslessly compress information relevant to answer derivation and synthesize a coherent final answer. Experiments across multiple open-source agents and benchmarks demonstrate up to 62% performance improvement with a 10--30% reduction in exploratory token consumption.

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Baixuan Li, Dingchu Zhang, Jialong Wu, Wenbiao Yin, Zhengwei Tao, Yida Zhao, Liwen Zhang, Haiyang Shen, Runnan Fang, Pengjun Xie, Jingren Zhou, Yong Jiang. 2025-10-28. ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking. https://arxiv.org/abs/2510.24698

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