Early-Token Confidence Predicts Reasoning Quality in Multi-Agent LLM Debate
Evaluating reasoning quality in multi-agent LLM systems is challenging, especially for open-ended tasks without reference answers. We investigate whether intrinsic confidence signals, token-level log-probabilities from decoding, can predict reasoning quality as assessed by LLM-as-judge evaluation. Using a debate-based essay scoring framework, we compare confidence proxies against rubric-based judge scores across two ASAP essay sets. We find that early-token confidence, particularly within the first few generated tokens, is consistently the strongest predictor of reasoning quality, outperforming full-sequence statistics. Analysis of log-probability trajectories shows that the opening phase...
| Authors | Ali Keramati, Justin Cheok, Jacob Horne et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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