The Deliberative Illusion: Diagnosing Factual Attrition and Stance Homogenization in Multi-Agent LLM Deliberation

2026-06-01

AI Summary

This work addresses a critical yet overlooked issue in multi-agent large language model (LLM) negotiations: the conflation of superficial consensus with genuine agreement, which often masks substantial loss of key facts and reduced stance diversity, thereby compromising reliability. To tackle this, the authors propose DelibTrace, a novel framework that decomposes negotiation topics into atomic issues, annotates essential facts, and tracks contextual evolution across dialogue turns. Leveraging this approach, they identify and formally characterize “deliberation hallucination”—a phenomenon wherein agents converge on misleading or incomplete agreements. The study introduces a new evaluation paradigm centered on factual retention rate and stance heterogeneity. Experiments reveal that up to 72% of critical facts can be lost during multi-round negotiations, consensus is heavily biased by base model priors, and even a single malicious agent can corrupt the shared context, leading to collectively degraded knowledge—“the more they negotiate, the less they know.”

Abstract

Multi-agent LLM systems often treat consensus as evidence of successful interaction. For deliberative problems, however, reliability depends on whether agents preserve the facts and viewpoints needed to interpret an issue. We identify the deliberative illusion: discussion produces (1) factual attrition, the progressive loss of issue-critical facts, alongside (2) stance homogenization, the collapse of diverse positions toward consensus. To measure this process, we introduce DelibTrace, a framework that decomposes each issue into atomic facts, labels issue-critical ones, distributes them across agents, and tracks their survival across discussion rounds. Across ethical and news-based deliberation with three representative LLM families, multi-agent discussion erases up to 72% of issue-critical facts. This loss is consequential: retained evidence can reconstruct the issue misleadingly, final stances remain anchored in base-model priors, and a single malicious agent can inject misinformation into the shrinking shared context. These results reveal a sharper risk: agents can agree more while knowing less. We call for evaluations that measure which facts, uncertainties, and legitimate disagreements survive interaction.

Problem

• deliberative illusion
• factual attrition
• stance homogenization
• multi-agent LLM
• consensus

Innovation

• deliberative illusion
• factual attrition
• stance homogenization
• DelibTrace
• multi-agent LLM