ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking
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.
69017d30646208eac0d1f3e7
Baixuan Li
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Dingchu Zhang
69017d30646208eac0d1f3e9
Jialong Wu
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2026-01-09T08:37:59.196Z
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Jialong Wu
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69017d30646208eac0d1f3ea
Wenbiao Yin
69017d30646208eac0d1f3eb
Zhengwei Tao
69017d30646208eac0d1f3ec
Yida Zhao
claimed_verified
2025-10-29T12:44:30.204Z
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Yida Zhao
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user
zhaoyd
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Liwen Zhang
69017d30646208eac0d1f3ee
Haiyang Shen
69017d30646208eac0d1f3ef
Runnan Fang
69017d30646208eac0d1f3f0
Pengjun Xie
69017d30646208eac0d1f3f1
Jingren Zhou
69017d30646208eac0d1f3f2
Yong Jiang
ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking
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.
ParallelMuse enhances problem-solving by efficiently integrating parallel thinking with deep exploration through uncertainty-guided path reuse and reasoning compression.
Functionality-Specified Partial Rollout Compressed Reasoning Aggregation uncertainty-guided path reuse reasoning redundancy information-seeking agents