arXiv:2606.10307v1 Announce Type: new Abstract: 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 strong
The rapid advancement and deployment of multi-agent LLM systems necessitates new methods for evaluating their performance, especially in open-ended reasoning tasks.
This research provides a novel intrinsic method for assessing LLM reasoning quality, moving beyond reliance on external judge evaluations and potentially accelerating agent development and reliability.
The ability to predict reasoning quality from early token confidence could fundamentally change how multi-agent LLM systems are debugged, optimized, and deployed, leading to more robust and trustworthy AI agents.
- · AI developers
- · LLM-as-judge platforms
- · Enterprise AI integration
- · AI ethics and safety researchers
- · Manual LLM evaluation methods
- · Systems with opaque reasoning processes
Improved methods for evaluating and debugging multi-agent LLM systems will emerge.
Faster iteration cycles and more reliable deployments of autonomous AI agents across various industries will follow.
The development of 'self-aware' AI agents capable of introspecting and reporting on their own reasoning fallibility could accelerate.
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Read at arXiv cs.CL