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arXiv · 2609.03416

Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection

Weijie Liu·Running Zhao·Wenhao Yuan·Jinfeng Xu·Zhanfeng Xu·Xiaoxi Zhang·Edith Cheuk-Han Ngai
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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 Dual-Detection 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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Weijie Liu, Running Zhao, Wenhao Yuan, Jinfeng Xu, Zhanfeng Xu, Xiaoxi Zhang, Edith Cheuk-Han Ngai. 2026-09-07. Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection. https://arxiv.org/abs/2609.03416

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