LAM-RAG: LoRA Messages for Multi-Document Retrieval-Augmented Generation

Multi-agent debate is built to reach consensus. Multi-document RAG often needs the opposite: when evidence is ambiguous or conflicting, different documents support different correct answers, all of which should be kept. Transcript-based debate works against this goal: from the same local answers, transcript exchange lowers answer-set F1 below the no-exchange level as agents copy their peers' answers and supported alternatives disappear. We propose LAM-RAG (LoRA-as-Message RAG), which keeps local answers and final aggregation in text but carries inter-agent influence as transient LoRA messages. A frozen translator maps each answer state to low-rank factors that a reliability-aware controller prunes and scales. Because a LoRA message exposes no copyable peer answers, it couples agents weakly: peer-answer adoption drops sharply and distinct supported answers survive. Yet the messages still carry useful information: on Qwen2.5-1.5B, one round of exchange raises F1 by 1.5–2.5 points over no exchange, and zero, random, or cross-question messages lower it by 1.3–1.9. On RAMDocs, AmbigDocs, and FaithEval with three backbones, LAM-RAG beats the strongest text-exchange or single-pass baseline by 2.86–5.20 average F1. Under added distractors or peers, LoRA messages degrade less than peer text.