LLMs as a Jury: Cross-Model Consensus Can Outperform Process Reward Models for LLM Reasoning
Selecting the correct answer from a pool of candidate reasoning chains is the engine of test-time scaling, yet the standard selectors each carry a cost: self-consistency inherits the errors of the single model it resamples, and trained reward models need labeled data and transfer poorly off-distribution. We study a third signal, free at inference time: cross-model consensus, the degree to which independently trained models, each solving the problem once, agree on a final answer. We treat the panel as an LLM-jury, in which the verification signal is the structure of agreement itself, with no model scoring another's work. Across seven benchmarks it selects correct answers better than self-c...
| Authors | Ning Liu |
|---|---|
| Primary category | cs.LG |
| Published | 11 July 2026 |
| Last updated at source | 17 August 2026 |
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