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
Published: 2025-10-28 17:51:50

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.