AI RESEARCH
LatentMem: Customizing Latent Memory for Multi-Agent Systems
arXiv CS.LG
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ArXi:2602.03036v2 Announce Type: replace-cross Large language model (LLM)-powered multi-agent systems (MAS) nstrate remarkable collective intelligence, wherein multi-agent memory serves as a pivotal mechanism for continual adaptation. However, existing multi-agent memory designs remain constrained by two fundamental bottlenecks: (i) memory homogenization arising from the absence of role-aware customization, and (ii) information overload induced by excessively fine-grained memory entries.