@inproceedings{shen-etal-2026-compass,
title = "{COMPASS}: Cognitive {MCTS}-Guided Process Alignment for Safe Search Agents",
author = "Shen, Wenkai and
Zhou, Pengyang and
Xu, Jiahe and
Qian, Jiaming and
He, Haozhe and
Huang, Zhihao and
Chen, Chaochao and
Zheng, Xiaolin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.647/",
pages = "13583--13609",
ISBN = "979-8-89176-462-0",
abstract = "LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmful intents may decompose into seemingly innocuous sub-queries that lead to unsafe outcomes. Existing alignment methods struggle to capture sparse safety signals and fail to supervise diverse violations across multi-step interactions. We propose COMPASS, a Cognitive MCTS-Guided Process Alignment framework designed to achieve robust safety alignment throughout the agent workflow while preserving general utility. COMPASS integrates cognitive tree exploration (CTE) to efficiently synthesize stealthy attack trajectories, and introspective step-wise alignment (ISA) to isolate risky intermediate actions for fine-grained process supervision. Empirical results show that COMPASS achieves a favorable safety-utility trade-off while requiring substantially less training data.The code is available on \href{https://github.com/crazy1212122/COMPASS.git}{github}."
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<abstract>LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmful intents may decompose into seemingly innocuous sub-queries that lead to unsafe outcomes. Existing alignment methods struggle to capture sparse safety signals and fail to supervise diverse violations across multi-step interactions. We propose COMPASS, a Cognitive MCTS-Guided Process Alignment framework designed to achieve robust safety alignment throughout the agent workflow while preserving general utility. COMPASS integrates cognitive tree exploration (CTE) to efficiently synthesize stealthy attack trajectories, and introspective step-wise alignment (ISA) to isolate risky intermediate actions for fine-grained process supervision. Empirical results show that COMPASS achieves a favorable safety-utility trade-off while requiring substantially less training data.The code is available on \hrefhttps://github.com/crazy1212122/COMPASS.gitgithub.</abstract>
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%0 Conference Proceedings
%T COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents
%A Shen, Wenkai
%A Zhou, Pengyang
%A Xu, Jiahe
%A Qian, Jiaming
%A He, Haozhe
%A Huang, Zhihao
%A Chen, Chaochao
%A Zheng, Xiaolin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F shen-etal-2026-compass
%X LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmful intents may decompose into seemingly innocuous sub-queries that lead to unsafe outcomes. Existing alignment methods struggle to capture sparse safety signals and fail to supervise diverse violations across multi-step interactions. We propose COMPASS, a Cognitive MCTS-Guided Process Alignment framework designed to achieve robust safety alignment throughout the agent workflow while preserving general utility. COMPASS integrates cognitive tree exploration (CTE) to efficiently synthesize stealthy attack trajectories, and introspective step-wise alignment (ISA) to isolate risky intermediate actions for fine-grained process supervision. Empirical results show that COMPASS achieves a favorable safety-utility trade-off while requiring substantially less training data.The code is available on \hrefhttps://github.com/crazy1212122/COMPASS.gitgithub.
%U https://aclanthology.org/2026.findings-emnlp.647/
%P 13583-13609
Markdown (Informal)
[COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents](https://aclanthology.org/2026.findings-emnlp.647/) (Shen et al., Findings 2026)
ACL
- Wenkai Shen, Pengyang Zhou, Jiahe Xu, Jiaming Qian, Haozhe He, Zhihao Huang, Chaochao Chen, and Xiaolin Zheng. 2026. COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 13583–13609, Budapest, Hungary. Association for Computational Linguistics.