Introduces protocols to aggregate transformed confidence signals from multiagent debates via soft voting or Bayesian fusion, yielding higher AUARC than single agents or standard baselines while keeping F1 stable across benchmarks.
arXiv preprint arXiv:2509.14034 , year=
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PEAR is a permutation-equivariant adaptive routing protocol for multi-agent LLM debate that reconfigures sparse topologies each round to improve accuracy over fixed debate baselines.
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Multiagent Protocols with Aggregated Confidence Signals
Introduces protocols to aggregate transformed confidence signals from multiagent debates via soft voting or Bayesian fusion, yielding higher AUARC than single agents or standard baselines while keeping F1 stable across benchmarks.
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PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate
PEAR is a permutation-equivariant adaptive routing protocol for multi-agent LLM debate that reconfigures sparse topologies each round to improve accuracy over fixed debate baselines.