arXiv:2606.10296v1 Announce Type: new Abstract: Multi-agent debate systems are typically evaluated only on whether the final answer is correct, overlooking the quality of the intermediate reasoning that debate is designed to produce. This paper studies the relationship between three signals in multi-agent debate: token-level log-probability distributions over reasoning tokens, LLM-as-judge rubric scores assigned to those tokens, and final task accuracy. We examine whether internal confidence signals predict externally evaluated reasoning quality, and whether either signal aligns with task corr
The rapid advancement of large language models necessitates better evaluation methods for complex AI systems, especially multi-agent architectures that are becoming more prevalent.
Improved diagnostic tools for multi-agent AI systems can lead to more robust, reliable, and trustworthy AI, accelerating their deployment in critical applications.
The ability to assess not just the final output but also the intermediate reasoning quality of multi-agent AI systems could fundamentally alter their development and auditing processes.
- · AI developers
- · AI auditors
- · Enterprises deploying AI
- · Research institutions
- · Black-box AI systems
- · Inadequate AI evaluation methods
This research provides a more granular understanding of AI agent performance beyond simple accuracy metrics.
Better diagnostics could lead to more efficient training and fine-tuning of multi-agent systems, improving their overall capabilities and trustworthiness.
The ability to 'diagnose' AI reasoning might lead to new techniques for AI alignment and safety, as internal confidence signals could be correlated with harmful reasoning paths.
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Read at arXiv cs.CL