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Abstract

Hallucinations in large language models (LLMs) pose heightened risks in multi-agent settings, where recursive agent interactions can propagate, reinforce, and amplify unsupported claims. This paper models hallucination as a systemlevel, time-evolving process across a network of interacting LLM agents, with nodes representing agents and edges encoding information exchange. The proposed formulation captures how hallucinated claims diffuse through communication topologies, intensify under adversarial perturbations, and affect collective reliability across reasoning rounds. To suppress error propagation, we introduce an interaction-aware control mechanism combining confidence-weighted aggregation, adaptive impact regulation, external claim verification, and selective isolation of unreliable agents. Experiments on TruthfulQA and TriviaQA show that the proposed method reduces hallucination by up to 39.0% relative to undefended multi-agent reasoning, improves factual accuracy from 0.79 to 0.87, and increases semantic consistency from 0.75 to 0.84. Under adversarial conditions, the proposed approach limits hallucination amplification to 1.08 compared with 1.45 without adaptive control, maintaining stable collective behavior across recursive interaction rounds. These results indicate that hallucination in multi-agent LLM systems is governed by both individual model reliability and systemlevel interaction dynamics, including communication topology, confidence coupling, and recursive information flow.