@inproceedings{zheng-etal-2026-p,
title = "{P}-Search: A Parallel Plan{--}Act{--}Verify Framework with Decoupled {GRPO} for Deep Research",
author = "Zheng, Pin and
Xue, Yibo and
Wang, Ming and
Kang, Yukun and
Zheng, Haonan and
Wang, Chengyu and
Peng, Jian and
Huang, Jun and
Xu, Changliang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.570/",
pages = "13074--13098",
ISBN = "979-8-89176-459-0",
abstract = "Open-web deep research is limited not only by how long an agent searches, but also by how it organizes evidence into independent, verifiable branches. Existing systems often scale single-path ReAct trajectories or long-context search, which can deepen one hypothesis while leaving alternative entities, constraints, or falsifying evidence underexplored. We propose P-Search, a parallel plan{--}act{--}verify framework that first builds a grounded background state, then decomposes the task into independently executable evidence branches, runs branch-specific search agents in parallel, and iteratively verifies unresolved constraints before final synthesis. For training, we avoid end-to-end credit assignment over a full multi-stage rollout. Instead, P-Search-React learns reusable grounded search-report behavior from raw-question search instances and is reused for branch execution and targeted supplementary search, while P-Search-Plan learns schema-constrained routing, decomposition, and verification decisions. We supervise this structure with star-shaped subgraph data and role-specific GRPO rewards. Experiments show that instantiating P-Search with open-source backbones such as GLM-5 and Tongyi-DR-30B improves their reported baselines on most open-web benchmarks, while the trained 8B P-Search-React + P-Search-Plan system reaches 59.2{\%} on GAIA and 70.0{\%} on Xbench-DeepSearch. Ablations confirm the complementary roles of Plan, ReAct, and Verify, as well as the transferability of the trained planner across ReAct backbones."
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<abstract>Open-web deep research is limited not only by how long an agent searches, but also by how it organizes evidence into independent, verifiable branches. Existing systems often scale single-path ReAct trajectories or long-context search, which can deepen one hypothesis while leaving alternative entities, constraints, or falsifying evidence underexplored. We propose P-Search, a parallel plan–act–verify framework that first builds a grounded background state, then decomposes the task into independently executable evidence branches, runs branch-specific search agents in parallel, and iteratively verifies unresolved constraints before final synthesis. For training, we avoid end-to-end credit assignment over a full multi-stage rollout. Instead, P-Search-React learns reusable grounded search-report behavior from raw-question search instances and is reused for branch execution and targeted supplementary search, while P-Search-Plan learns schema-constrained routing, decomposition, and verification decisions. We supervise this structure with star-shaped subgraph data and role-specific GRPO rewards. Experiments show that instantiating P-Search with open-source backbones such as GLM-5 and Tongyi-DR-30B improves their reported baselines on most open-web benchmarks, while the trained 8B P-Search-React + P-Search-Plan system reaches 59.2% on GAIA and 70.0% on Xbench-DeepSearch. Ablations confirm the complementary roles of Plan, ReAct, and Verify, as well as the transferability of the trained planner across ReAct backbones.</abstract>
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%0 Conference Proceedings
%T P-Search: A Parallel Plan–Act–Verify Framework with Decoupled GRPO for Deep Research
%A Zheng, Pin
%A Xue, Yibo
%A Wang, Ming
%A Kang, Yukun
%A Zheng, Haonan
%A Wang, Chengyu
%A Peng, Jian
%A Huang, Jun
%A Xu, Changliang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zheng-etal-2026-p
%X Open-web deep research is limited not only by how long an agent searches, but also by how it organizes evidence into independent, verifiable branches. Existing systems often scale single-path ReAct trajectories or long-context search, which can deepen one hypothesis while leaving alternative entities, constraints, or falsifying evidence underexplored. We propose P-Search, a parallel plan–act–verify framework that first builds a grounded background state, then decomposes the task into independently executable evidence branches, runs branch-specific search agents in parallel, and iteratively verifies unresolved constraints before final synthesis. For training, we avoid end-to-end credit assignment over a full multi-stage rollout. Instead, P-Search-React learns reusable grounded search-report behavior from raw-question search instances and is reused for branch execution and targeted supplementary search, while P-Search-Plan learns schema-constrained routing, decomposition, and verification decisions. We supervise this structure with star-shaped subgraph data and role-specific GRPO rewards. Experiments show that instantiating P-Search with open-source backbones such as GLM-5 and Tongyi-DR-30B improves their reported baselines on most open-web benchmarks, while the trained 8B P-Search-React + P-Search-Plan system reaches 59.2% on GAIA and 70.0% on Xbench-DeepSearch. Ablations confirm the complementary roles of Plan, ReAct, and Verify, as well as the transferability of the trained planner across ReAct backbones.
%U https://aclanthology.org/2026.emnlp-main.570/
%P 13074-13098
Markdown (Informal)
[P-Search: A Parallel Plan–Act–Verify Framework with Decoupled GRPO for Deep Research](https://aclanthology.org/2026.emnlp-main.570/) (Zheng et al., EMNLP 2026)
ACL
- Pin Zheng, Yibo Xue, Ming Wang, Yukun Kang, Haonan Zheng, Chengyu Wang, Jian Peng, Jun Huang, and Changliang Xu. 2026. P-Search: A Parallel Plan–Act–Verify Framework with Decoupled GRPO for Deep Research. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 13074–13098, Budapest, Hungary. Association for Computational Linguistics.