EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory
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Large language model-based multi-agent systems (LLM-MAS) are designed through roles, debate protocols, and aggregation rules. These choices create implicit social expectations: agents may be expected to trust, challenge, defer to, or collaborate with peers.
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