Jielong Tang, Ling-An Zeng, Xujie Yuan, Xiao Dong, Zhiqian Xia, Qingfeng Li, Shixiong Kai, Kaichao Liang · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29995 · open-access PDF
对于多式代理人来说,长期记忆仍然具有挑战性,因为它必须保持细粒的视觉证据,同时在整个会话中跟踪实体进化。现有的抽象记忆将图像压缩成文本,通常会抛弃视觉上的歧视细节,而像素保存记忆保持原始观察,但难以可靠地将多样观察与持久的实体身份联系起来,并跟踪其进化状态。
Machine translation (Meta M2M100); the English original follows.
Long-term memory for multimodal agents remains challenging, as it must preserve fine-grained visual evidence while tracking entity evolution across sessions. Existing abstraction-based memories compress images into text, often discarding visually discriminative details, whereas pixel-preserving memories retain raw observations but struggle to reliably associate diverse observations with persistent entity identities and track their evolving states. To address these limitations, we propose MHG-Mem, a multimodal hypergraph memory that jointly organizes entity identity, state evolution, and proven
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