@inproceedings{zhou-etal-2026-evidence,
title = "Evidence Laundering: Deep Research Agents Mistake Correlated Sources for Independent Evidence",
author = "Zhou, Ruihua and
Huang, Hao and
Zheng, Yijie and
Luo, JiaTang and
Li, Yunpeng and
Liu, Yuling and
Du, Dan",
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.509/",
pages = "11682--11693",
ISBN = "979-8-89176-459-0",
abstract = "Deep research agents often rely on agreement among retrieved documents when deciding whether a claim is reliable. This heuristic breaks when the agreeing documents are not independent: several independent reports can support a claim, but several copies of the same false claim should not. We study evidence laundering, a content-only attack that publishes one false claim across paraphrased pages that appear to provide support from multiple sources. We evaluate this failure with deterministic matched corpora that present the same false claim and supporting propositions as a single source, a paraphrased correlated cluster, or an apparent independence control, with matched retrieval exposure. Experiments show that agents often assign laundered clusters credence comparable to the apparent independence control and become more willing to publish the false claim. Within the matched evaluation, a structural provenance gate substantially reduces this failure, suggesting that source independence modeling should precede answer synthesis."
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<abstract>Deep research agents often rely on agreement among retrieved documents when deciding whether a claim is reliable. This heuristic breaks when the agreeing documents are not independent: several independent reports can support a claim, but several copies of the same false claim should not. We study evidence laundering, a content-only attack that publishes one false claim across paraphrased pages that appear to provide support from multiple sources. We evaluate this failure with deterministic matched corpora that present the same false claim and supporting propositions as a single source, a paraphrased correlated cluster, or an apparent independence control, with matched retrieval exposure. Experiments show that agents often assign laundered clusters credence comparable to the apparent independence control and become more willing to publish the false claim. Within the matched evaluation, a structural provenance gate substantially reduces this failure, suggesting that source independence modeling should precede answer synthesis.</abstract>
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%0 Conference Proceedings
%T Evidence Laundering: Deep Research Agents Mistake Correlated Sources for Independent Evidence
%A Zhou, Ruihua
%A Huang, Hao
%A Zheng, Yijie
%A Luo, JiaTang
%A Li, Yunpeng
%A Liu, Yuling
%A Du, Dan
%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 zhou-etal-2026-evidence
%X Deep research agents often rely on agreement among retrieved documents when deciding whether a claim is reliable. This heuristic breaks when the agreeing documents are not independent: several independent reports can support a claim, but several copies of the same false claim should not. We study evidence laundering, a content-only attack that publishes one false claim across paraphrased pages that appear to provide support from multiple sources. We evaluate this failure with deterministic matched corpora that present the same false claim and supporting propositions as a single source, a paraphrased correlated cluster, or an apparent independence control, with matched retrieval exposure. Experiments show that agents often assign laundered clusters credence comparable to the apparent independence control and become more willing to publish the false claim. Within the matched evaluation, a structural provenance gate substantially reduces this failure, suggesting that source independence modeling should precede answer synthesis.
%U https://aclanthology.org/2026.emnlp-main.509/
%P 11682-11693
Markdown (Informal)
[Evidence Laundering: Deep Research Agents Mistake Correlated Sources for Independent Evidence](https://aclanthology.org/2026.emnlp-main.509/) (Zhou et al., EMNLP 2026)
ACL