Peer Memory Changes Who LLM Agents Consult Without a Detected Gain in Answer Accuracy

원문 초록

Multi-agent LLM systems often reuse the same agents across tasks, creating an opportunity to draw on past interactions when deciding whom to consult. We study this form of memory with PeerMem, which saves MACE's LinUCB state and reuses it when the same agents return. Using six agents, a fixed set of 80 HotpotQA questions, and 12 paired lineages per model, we compare cold, warm, and shuffled initialization in an answer-exchange protocol with reference-answer feedback. Under stable evidence access, warm memory lowers pooled hindsight regret and increases consultation of other evidence holders on Qwen3-32B and Gemma-4-31B-it, while final token F1 differences remain uncertain. Qwen interaction traces further show that warm agents more often encounter peer answers that score above their own prior answers. We extend the evaluation to Qwen3.5-9B using freshly learned memory to compare warm and cold initialization under reassigned and paired stable roles. The primary changed-minus-stable final-F1 interaction is -1.38 points, with a 95% interval of [-3.50,+0.73]. Together, these experiments trace the effects of peer memory through consultation, answer revision, and final task performance.