ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking
Authors: name: Baixuan Li, name: Dingchu Zhang, name: Jialong Wu, name: Wenbiao Yin, name: Zhengwei Tao, name: Yida Zhao, name: Liwen Zhang, name: Haiyang Shen, name: Runnan Fang, name: Pengjun Xie, name: Jingren Zhou, name: Yong Jiang
Organization: TongyiLab

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.