@inproceedings{liu-etal-2026-dude,
title = "Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection",
author = "Liu, Weijie and
Zhao, Running and
Yuan, Wenhao and
Xu, Jinfeng and
Xu, Zhanfeng and
Zhang, Xiaoxi and
Ngai, Edith Cheuk-Han",
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.1017/",
pages = "23371--23389",
ISBN = "979-8-89176-459-0",
abstract = "LLM-empowered paper-code discrepancy detection has received growing concern since the scaling of research submissions exceeds the manual review capability. However, the limited context capacity and one-sided discrepancy detection of existing single-agent LLM paradigms lead to an inferior recall performance in detecting discrepancies. In this paper, we propose **Dude**, the first **Du**al-**De**tection Multi-Agent System for paper-code discrepancy detection. We discover that the granularity asymmetry of the paper-language and code-language introduces over-interpretation and over-reporting challenges in a multi-agent system design for discrepancy detection, resulting in increasing false positives. To address this, we propose a granularity-aligned negotiation and a two-stage salience-filtering mechanism in Dude, which effectively prevents agents from falsely reporting discrepancies. Experimental results in real-world paper-code discrepancy datasets showcase Dude{'}s significant recall and precision improvement by up to **22.8{\%}**, increasing F1 score by up to **18.7{\%}** compared to baseline methods."
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<abstract>LLM-empowered paper-code discrepancy detection has received growing concern since the scaling of research submissions exceeds the manual review capability. However, the limited context capacity and one-sided discrepancy detection of existing single-agent LLM paradigms lead to an inferior recall performance in detecting discrepancies. In this paper, we propose **Dude**, the first **Du**al-**De**tection Multi-Agent System for paper-code discrepancy detection. We discover that the granularity asymmetry of the paper-language and code-language introduces over-interpretation and over-reporting challenges in a multi-agent system design for discrepancy detection, resulting in increasing false positives. To address this, we propose a granularity-aligned negotiation and a two-stage salience-filtering mechanism in Dude, which effectively prevents agents from falsely reporting discrepancies. Experimental results in real-world paper-code discrepancy datasets showcase Dude’s significant recall and precision improvement by up to **22.8%**, increasing F1 score by up to **18.7%** compared to baseline methods.</abstract>
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%0 Conference Proceedings
%T Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection
%A Liu, Weijie
%A Zhao, Running
%A Yuan, Wenhao
%A Xu, Jinfeng
%A Xu, Zhanfeng
%A Zhang, Xiaoxi
%A Ngai, Edith Cheuk-Han
%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 liu-etal-2026-dude
%X LLM-empowered paper-code discrepancy detection has received growing concern since the scaling of research submissions exceeds the manual review capability. However, the limited context capacity and one-sided discrepancy detection of existing single-agent LLM paradigms lead to an inferior recall performance in detecting discrepancies. In this paper, we propose **Dude**, the first **Du**al-**De**tection Multi-Agent System for paper-code discrepancy detection. We discover that the granularity asymmetry of the paper-language and code-language introduces over-interpretation and over-reporting challenges in a multi-agent system design for discrepancy detection, resulting in increasing false positives. To address this, we propose a granularity-aligned negotiation and a two-stage salience-filtering mechanism in Dude, which effectively prevents agents from falsely reporting discrepancies. Experimental results in real-world paper-code discrepancy datasets showcase Dude’s significant recall and precision improvement by up to **22.8%**, increasing F1 score by up to **18.7%** compared to baseline methods.
%U https://aclanthology.org/2026.emnlp-main.1017/
%P 23371-23389
Markdown (Informal)
[Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection](https://aclanthology.org/2026.emnlp-main.1017/) (Liu et al., EMNLP 2026)
ACL