arXiv:2606.00820v1 Announce Type: new Abstract: Multi-agent debate (MAD) is a promising strategy for improving LLM reasoning, but when agents converge on a shared answer, it is unclear whether that convergence reflects genuine deliberation or social compliance. We show that the conventional answer flip rate conflates three distinct mechanisms: spontaneous instability, stance-induced conformity, and reasoning-induced persuasion. Our three-source decomposition framework isolates each through controlled counterfactual conditions. In the primary MMLU-Pro setting, 37% of agent-question observations
The proliferation of Large Language Models (LLMs) and their application in multi-agent systems necessitates deeper understanding of their internal decision-making processes, as current approaches have limitations.
Understanding how LLM agents converge on answers is critical for evaluating the reliability and trustworthiness of AI systems deployed for reasoning, debate, and complex problem-solving.
This research provides a framework to differentiate between genuine persuasive reasoning and mere social conformity in multi-agent LLM debates, allowing for more robust AI system design and evaluation.
- · AI researchers
- · Developers of multi-agent LLM systems
- · Users of AI-driven decision support tools
- · AI systems relying on superficial agreement
- · Uncritical adoption of multi-agent LLM outputs
Improved methods for evaluating and training multi-agent LLM systems for genuine reasoning.
Development of agent architectures that explicitly minimize conformity and maximize critical deliberation.
Enhanced trust and broader adoption of AI for complex decision-making, as systems become more auditable and reliable in their reasoning.
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Read at arXiv cs.CL