Stay Focused: Problem Drift in Multi-Agent Debate

Jonas Becker, Lars Benedikt Kaesberg, Andreas Stephan, Jan Philip Wahle, Terry Ruas, Bela Gipp


Abstract
Multi-agent debate – multiple instances of large language models discussing problems in turn-based interaction – has shown promise for solving knowledge and reasoning tasks. However, these methods show limitations when solving complex problems that require longer reasoning chains. We analyze how multi-agent debate drifts away from the initial problem over multiple turns, thus harming task performance. We define this phenomenon as problem drift and quantify its presence across ten tasks (i.e., three generative, three knowledge, three reasoning, and one instruction-following task). We find that generative tasks drift often due to the subjectivity of the answer space (76-89%), compared to high-complexity tasks (7-21%). To identify the reasons, eight human experts analyze 170 multi-agent debates suffering from problem drift. We find the most common issues related to this drift are the lack of progress (35% of cases), low-quality feedback (26% of cases), and a lack of clarity (25% of cases). We propose DRIFTJudge, an LLM-as-a-judge method, as a first baseline to detect problem drift. We also propose DRIFTPolicy, which mitigates 31% of problem drift cases. Our study is a step toward understanding a key limitation of multi-agent debate, highlighting why longer debates can harm task performance and how problem drift could be addressed.
Anthology ID:
2026.findings-eacl.268
Volume:
Findings of the Association for Computational Linguistics: EACL 2026
Month:
March
Year:
2026
Address:
Rabat, Morocco
Editors:
Vera Demberg, Kentaro Inui, Lluís Marquez
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5068–5102
Language:
URL:
https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.268/
DOI:
Bibkey:
Cite (ACL):
Jonas Becker, Lars Benedikt Kaesberg, Andreas Stephan, Jan Philip Wahle, Terry Ruas, and Bela Gipp. 2026. Stay Focused: Problem Drift in Multi-Agent Debate. In Findings of the Association for Computational Linguistics: EACL 2026, pages 5068–5102, Rabat, Morocco. Association for Computational Linguistics.
Cite (Informal):
Stay Focused: Problem Drift in Multi-Agent Debate (Becker et al., Findings 2026)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.268.pdf
Checklist:
 2026.findings-eacl.268.checklist.pdf