Confidence Composition for Multiagent Language Model Systems
Multiagent language model systems, such as collaborative reasoning and debate, produce multiple correlated candidate answers and confidence signals. However, these signals are usually calibrated only at the individual agent level, and provide no principled confidence estimate for the system's final answer. We formulate this as a confidence composition problem where combining confidence across agents and reasoning stages while preserving both selective utility and probabilistic reliability. We study confidence-aware routing and log-odds pooling protocols that select among candidate answers and output a system-level confidence. Across five benchmarks, 30 heterogeneous and homogeneous model ...
| Authors | Ali Elahi, Michael J. Curry, Barbara Di Eugenio |
|---|---|
| Primary category | cs.AI |
| Published | 11 June 2026 |
| Last updated at source | 21 September 2026 |
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