@inproceedings{huang-etal-2026-deepfact,
title = "{D}eep{F}act: Co-Evolving Benchmarks and Agents for Deep Research Factuality",
author = "Huang, Yukun and
Ribeiro, Leonardo F. R. and
Hardalov, Momchil and
Dhingra, Bhuwan and
Dreyer, Markus and
Saligrama, Venkatesh",
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.1586/",
pages = "34356--34386",
ISBN = "979-8-89176-390-6",
abstract = "Search-augmented LLM agents can produce deep research reports (DRRs), but verifying claim-level factuality remains challenging. Existing fact-checkers usually target general-domain atomic claims, and there is no benchmark to test whether such verifiers transfer to DRRs.Yet building such a benchmark for DRR fact-checkers is itself difficult because it requires expert judgments over cognitively demanding, domain-specific claims.In a controlled study with PhD-level specialists, unassisted experts achieve only 60.8{\%} accuracy on hidden known-answer claims. We therefore propose evolving benchmarking via **Audit-then-Score** (**AtS**), in which labels and rationales remain revisable: when a verifier disagrees with the current benchmark, it submits evidence; an auditor adjudicates the dispute; and accepted revisions update the benchmark before scoring. After three additional **AtS** rounds, expert accuracy rises to 90.9{\%}, showing that experts are better auditors than one-shot labelers.We instantiate **AtS** as **DeepFactBench**, a versioned DRR factuality benchmark with auditable rationales, and introduce **DeepFactEval**, a claim-level verifier.On the frozen **DeepFactBench** release, **DeepFactEval** achieves 83.4{\%} accuracy, outperforming the best prior deep-research and traditional fact-checkers by 14.3 and 24.9 points, respectively, and transferring well to external factuality datasets."
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<abstract>Search-augmented LLM agents can produce deep research reports (DRRs), but verifying claim-level factuality remains challenging. Existing fact-checkers usually target general-domain atomic claims, and there is no benchmark to test whether such verifiers transfer to DRRs.Yet building such a benchmark for DRR fact-checkers is itself difficult because it requires expert judgments over cognitively demanding, domain-specific claims.In a controlled study with PhD-level specialists, unassisted experts achieve only 60.8% accuracy on hidden known-answer claims. We therefore propose evolving benchmarking via **Audit-then-Score** (**AtS**), in which labels and rationales remain revisable: when a verifier disagrees with the current benchmark, it submits evidence; an auditor adjudicates the dispute; and accepted revisions update the benchmark before scoring. After three additional **AtS** rounds, expert accuracy rises to 90.9%, showing that experts are better auditors than one-shot labelers.We instantiate **AtS** as **DeepFactBench**, a versioned DRR factuality benchmark with auditable rationales, and introduce **DeepFactEval**, a claim-level verifier.On the frozen **DeepFactBench** release, **DeepFactEval** achieves 83.4% accuracy, outperforming the best prior deep-research and traditional fact-checkers by 14.3 and 24.9 points, respectively, and transferring well to external factuality datasets.</abstract>
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%0 Conference Proceedings
%T DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality
%A Huang, Yukun
%A Ribeiro, Leonardo F. R.
%A Hardalov, Momchil
%A Dhingra, Bhuwan
%A Dreyer, Markus
%A Saligrama, Venkatesh
%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-deepfact
%X Search-augmented LLM agents can produce deep research reports (DRRs), but verifying claim-level factuality remains challenging. Existing fact-checkers usually target general-domain atomic claims, and there is no benchmark to test whether such verifiers transfer to DRRs.Yet building such a benchmark for DRR fact-checkers is itself difficult because it requires expert judgments over cognitively demanding, domain-specific claims.In a controlled study with PhD-level specialists, unassisted experts achieve only 60.8% accuracy on hidden known-answer claims. We therefore propose evolving benchmarking via **Audit-then-Score** (**AtS**), in which labels and rationales remain revisable: when a verifier disagrees with the current benchmark, it submits evidence; an auditor adjudicates the dispute; and accepted revisions update the benchmark before scoring. After three additional **AtS** rounds, expert accuracy rises to 90.9%, showing that experts are better auditors than one-shot labelers.We instantiate **AtS** as **DeepFactBench**, a versioned DRR factuality benchmark with auditable rationales, and introduce **DeepFactEval**, a claim-level verifier.On the frozen **DeepFactBench** release, **DeepFactEval** achieves 83.4% accuracy, outperforming the best prior deep-research and traditional fact-checkers by 14.3 and 24.9 points, respectively, and transferring well to external factuality datasets.
%U https://aclanthology.org/2026.acl-long.1586/
%P 34356-34386
Markdown (Informal)
[DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality](https://aclanthology.org/2026.acl-long.1586/) (Huang et al., ACL 2026)
ACL
- Yukun Huang, Leonardo F. R. Ribeiro, Momchil Hardalov, Bhuwan Dhingra, Markus Dreyer, and Venkatesh Saligrama. 2026. DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 34356–34386, San Diego, California, United States. Association for Computational Linguistics.