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

Researchers propose Dude, the first dual-detection multi-agent system for paper-code discrepancy detection, which improves recall and precision by up to 22.8% and increases F1 score by up to 18.7% over baseline methods. The system addresses granularity asymmetry between paper-language and code-language with granularity-aligned negotiation and two-stage salience-filtering to reduce false positives.

arXiv:2609.03416v1 Announce Type: new 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.