When Truth Is Distributed: Misinformation Derails Collective Fact Recovery in LLM-Based Multi-Agent Systems

2026-08-04

AI Summary

This study investigates how localized misinformation propagates through interactions in large language model (LLM) multi-agent systems and undermines collective fact-recovery capabilities. To this end, the authors propose the Hi-Agreement evaluation framework, which compares scenarios of honest collaboration against those where key evidence holders introduce deceptive statements within a controlled environment. By integrating multi-stage voting, testimony adoption tracking, and evidence provenance analysis, the framework elucidates the mechanisms of distributed information aggregation. Experiments reveal, for the first time, that a single false testimony is more readily adopted and propagates more widely than truthful statements, exerting persistent influence even after the deceiving agent withdraws. Across 120 five-agent scenarios, collective fact-recovery rates plummeted from 72.50% to 14.17%, highlighting the system’s acute vulnerability to deception; while observers without direct evidence can mitigate erroneous consensus, they fail to significantly enhance truth recovery.

Abstract

LLM-based multi-agent systems promise effective collaborative reasoning, but communication may amplify local errors into collective risks. Existing evaluations emphasize final outcomes, leaving the reliability and propagation dynamics of distributed information aggregation unclear. We introduce Hi-Agreement, a controlled evaluation framework that strictly pairs all-honest collaboration with controlled deception by a key evidence holder and analyzes the aggregation process through multi-stage voting, testimony adoption, and evidence-root lineage propagation. Using 120 five-agent object-movement environments where partial observations jointly determine a unique endpoint, we evaluate 3 homogeneous LLM-based multi-agent systems. Across these paired conditions, aggregate truth recovery falls from 72.50% to 14.17%, with significant declines for every system. Process tracing and exit ablations show that a single false testimony is adopted more readily than truthful testimony, propagates to higher orders, and persists through honest agents after the deceiver exits. Observers without first-hand evidence suppress incorrect consensus but do not improve truth recovery. Together, these findings reveal both the fragility of distributed fact recovery and its underlying mechanism: false evidence gains collective influence through its adoption and continued propagation by other agents after entering communication.

Problem

• misinformation
• multi-agent systems
• fact recovery
• distributed reasoning
• LLM-based collaboration

Innovation

• multi-agent systems
• misinformation propagation
• collective fact recovery
• LLM-based reasoning
• controlled evaluation framework