@inproceedings{huang-etal-2026-deep,
title = "Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety",
author = "Huang, Wei-Chieh and
Zou, Henry Peng and
Wu, Yaozu and
Li, Dongyuan and
Chen, Yankai and
Zhang, Weizhi and
Li, Yangning and
Zangari, Angelo and
Guo, Jizhou and
Miao, Chunyu and
Fang, Liancheng and
He, Langzhou and
Li, Yinghui and
Jiang, Renhe and
Yu, Philip S.",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2010/",
pages = "43403--43446",
ISBN = "979-8-89176-390-6",
abstract = "Deep research frameworks have shown promising capabilities in synthesizing comprehensive reports from web sources. While deep research possesses significant potential to address complex issues through planning and research cycles, existing frameworks are deficient in sufficient evaluation procedures and stage-specific protections. They typically treat evaluation as exact match accuracy of question-answering, but overlook crucial aspects of report quality such as credibility, coherence, breadth, depth, and safety. This oversight may result in hazardous or malicious sources being integrated into the final report. To address this, we introduce DeepResearchGuard, a framework featuring four-stage safeguards with open-domain evaluation, and DRSafeBench, a novel stage-wise safety benchmark. Evaluating across GPT-4o, o4-mini, Gemini-2.5-flash, DeepSeek-v3, and GPT-5, DeepResearchGuard improves defense success rates by an absolute 16.53{\%} while reducing over-refusal rates to approximately 6{\%}. Through extensive experiments, we show that DeepResearchGuard enables comprehensive open-domain evaluation and stage-aware defenses that effectively block harmful content propagation, while systematically improving report quality without excessive over-refusal rates."
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<abstract>Deep research frameworks have shown promising capabilities in synthesizing comprehensive reports from web sources. While deep research possesses significant potential to address complex issues through planning and research cycles, existing frameworks are deficient in sufficient evaluation procedures and stage-specific protections. They typically treat evaluation as exact match accuracy of question-answering, but overlook crucial aspects of report quality such as credibility, coherence, breadth, depth, and safety. This oversight may result in hazardous or malicious sources being integrated into the final report. To address this, we introduce DeepResearchGuard, a framework featuring four-stage safeguards with open-domain evaluation, and DRSafeBench, a novel stage-wise safety benchmark. Evaluating across GPT-4o, o4-mini, Gemini-2.5-flash, DeepSeek-v3, and GPT-5, DeepResearchGuard improves defense success rates by an absolute 16.53% while reducing over-refusal rates to approximately 6%. Through extensive experiments, we show that DeepResearchGuard enables comprehensive open-domain evaluation and stage-aware defenses that effectively block harmful content propagation, while systematically improving report quality without excessive over-refusal rates.</abstract>
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%0 Conference Proceedings
%T Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety
%A Huang, Wei-Chieh
%A Zou, Henry Peng
%A Wu, Yaozu
%A Li, Dongyuan
%A Chen, Yankai
%A Zhang, Weizhi
%A Li, Yangning
%A Zangari, Angelo
%A Guo, Jizhou
%A Miao, Chunyu
%A Fang, Liancheng
%A He, Langzhou
%A Li, Yinghui
%A Jiang, Renhe
%A Yu, Philip S.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F huang-etal-2026-deep
%X Deep research frameworks have shown promising capabilities in synthesizing comprehensive reports from web sources. While deep research possesses significant potential to address complex issues through planning and research cycles, existing frameworks are deficient in sufficient evaluation procedures and stage-specific protections. They typically treat evaluation as exact match accuracy of question-answering, but overlook crucial aspects of report quality such as credibility, coherence, breadth, depth, and safety. This oversight may result in hazardous or malicious sources being integrated into the final report. To address this, we introduce DeepResearchGuard, a framework featuring four-stage safeguards with open-domain evaluation, and DRSafeBench, a novel stage-wise safety benchmark. Evaluating across GPT-4o, o4-mini, Gemini-2.5-flash, DeepSeek-v3, and GPT-5, DeepResearchGuard improves defense success rates by an absolute 16.53% while reducing over-refusal rates to approximately 6%. Through extensive experiments, we show that DeepResearchGuard enables comprehensive open-domain evaluation and stage-aware defenses that effectively block harmful content propagation, while systematically improving report quality without excessive over-refusal rates.
%U https://aclanthology.org/2026.acl-long.2010/
%P 43403-43446
Markdown (Informal)
[Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety](https://aclanthology.org/2026.acl-long.2010/) (Huang et al., ACL 2026)
ACL
- Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhang, Yangning Li, Angelo Zangari, Jizhou Guo, Chunyu Miao, Liancheng Fang, Langzhou He, Yinghui Li, Renhe Jiang, and Philip S. Yu. 2026. Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 43403–43446, San Diego, California, United States. Association for Computational Linguistics.