SOURCE-LINKED INTELLIGENCE

Candidate supply and answer selection shape the value of LLM judging in multi-agent systems

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

Multi-agent systems (MAS) sometimes already have the potential to answer correctly, but still report a wrong answer. Explaining this outcome is difficult because generation, communication and final answer-selection rules usually change simultaneously. We conceptualize multi-agent reasoning as an evolutionary pipeline of candidate generation, peer communication and terminal selection, wherein consensus without quality control can exhibit patterns of memetic drift. We study two questions: (1) when an LLM judge provides effective selection pressure by supplying a signal of answer correctness for

Read original source ↗ Open in workspace

recordType

paper

region

Global