Collaborative Memory for Multi-Agent VLM Systems

The paper introduces a framework for collaborative memory in multi‑agent vision‑language model (VLM) systems, addressing how agents share and update visual context across distributed perception and reasoning tasks. It outlines a memory hierarchy, cross‑agent sharing protocols, and consistency mechanisms to reconcile differing interpretations and recover missing visual information. The design emphasizes preserving not only raw images or textual summaries but also the dependencies among observations, interpretations, and subsequent reasoning, thereby shaping information flow across agents.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• agents
• multimodal