arXiv AI
Who Convinced Whom? Tracing How LLMs Change Their Minds in Multi-Agent Systems
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arXiv AI
Jun 2
Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity
arXiv:2606. 01637v1 Announce Type: cross Abstract: Large language models are increasingly used in multi-agent systems, where they see and respond to other agents' answers.
arXiv AI
Oct 15
Beyond Answer Sharing: TrueMAD for Targeted Peer Engagement in Multi-Agent Debate
arXiv:2610.18240v1 Announce Type: new Abstract: Multi-agent debate (MAD) aims to improve large language model reasoning by exposing agents to alternative solutions and enabling iterative revision. Ho...
arXiv AI
Sep 10
A Layered Analysis of Disagreement And Answer Quality in Multi-Agent LLM Debate
arXiv:2609.08016v1 Announce Type: new Abstract: Multi-agent debate, in which several LLMs exchange arguments before answering, is widely assumed to improve answer quality by surfacing genuine disagre...
arXiv AI
Jun 2
Demystifying Multi-Agent Debate: The Role of Confidence and Diversity
arXiv:2601. 19921v2 Announce Type: replace-cross Abstract: Multi-agent debate (MAD) is widely used to improve large language model (LLM) performance through test-time scaling, yet recent work shows that vanilla MAD often underperforms simple majority vote despite higher computational cost.
arXiv AI
Oct 1
When Order Matters: First-Speaker Bias and Mitigation through Personality in Sequential Multi-Agent Debate
arXiv:2609.38964v1 Announce Type: new Abstract: Multi-agent debate (MAD) is often used to improve large language model (LLM) reasoning, but sequential debate is rarely a neutral aggregator of agents'...
arXiv Computation and Language
Oct 1
Multi-agent discussion gains less when dissent is withheld
arXiv:2609.38324v1 Announce Type: cross Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on w...