Candidate Supply and Answer Selection Shape the Value of LLM Judging in Multi-Agent Systems
Score
73系统拆解多智能体推理环节并揭示裁判可靠性的条件依赖性，提供了以往研究未覆盖的诊断视角。
基于多个基准和数万问题的大规模实验，方法论严谨，发现具有统计支持。
提出的频率+裁判组合选择规则可直接用于多智能体系统设计，显著提升准确率。
论文上线时间较新，主题契合当前多Agent与LLM评估的研究热点。
来源为arXiv预印本，经过初步审阅但尚未正式同行评议，作者团队未披露但数据公开。
Base 81.5 × T2 Source weight ×0.9 = 73
Curation line 70Selected
Why selected
The paper is rigorous with large-scale data, and its conclusions directly guide selection-strategy optimization in multi-agent systems, offering valuable reference for agent reliability research.
AI commentary
「This paper cleanly decomposes failure causes in multi-agent systems, especially relevant for those building agent frameworks and voting/selection mechanisms. It reminds us that simply increasing model count is not enough; when the correct answer already exists in the candidate pool, the real bottleneck may lie in the selection rule.」
Quick overview
This paper conceptualizes multi-agent reasoning as an evolutionary pipeline of candidate generation, peer communication, and terminal selection, showing that correct candidates often already exist yet the system still reports wrong answers. Based on 16,278 questions, 81,390 replayed candidate pools, and 15,336 judge reliability tests, they propose a combined rule using answer frequency plus judge evaluation, lifting accuracy from 63.82% to 70.82-70.95%, mainly by rescuing correct answers outnumbered by popular errors. Judge reliability is not a fixed trait but varies with task, generator, and answer rarity. The findings establish a diagnostic basis for designing multi-agent architectures that prevent loss of generated correct answers.
- Correct candidates are often already generated, yet the multi-agent system still converges to a wrong answer;
- LLM judge reliability varies with task, generator, and answer rarity, not a fixed trait;
- Combining answer frequency with judge evaluation improves accuracy by about 7 points across benchmarks;
- The value of generating more candidates depends on whether extra samples make correct answers present, frequent, or recognizable.
Full text
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