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