C3M is a cross‑session multimodal memory system designed for long‑horizon tasks that must preserve and retrieve evidence across sessions within a limited, query‑blind memory budget. It maintains a bounded active index of source text‑image evidence, using relation‑aware updates to keep safe redundancy while preserving complementary and incompatible records. At query time, budgeted routing selects useful index pages and expands their associated source evidence under a fixed reader budget, creating a compact, provenance‑preserving memory that retains temporal distinctions and source links for reliable downstream reasoning.
By Xueshu Chen, Yan Wang, Zihao Xue, Jiefu Li, Zhenfang Liu, Jayden Chen, Zhen Bi, Jungang Lou
arXiv:2607. 28678v1 Announce Type: new Abstract: Multimodal agents operating in long-horizon environments must build and continually update multimedia memories to support entity-consistent, temporally grounded reasoning.
By Xinkui Zhao, Enbo Chen, Yifan Zhang, Chang Liu, Guanjie Cheng, Naibo Wang, Yueshen Xu
GraphMemix introduces a combinatorial‑optimization graph memory framework that constructs query‑aware evidence forests for long‑term multimodal agent memory. It expands seed memories via schema and semantic relations, decouples memory support from relation verification to reduce redundancy, and optimizes a forest‑format context within a maximum evidence budget. Experiments on four benchmarks show significant accuracy gains and a new Pareto frontier between accuracy and lifecycle cost.
By Geng Li, Yuhao Wang, Dong Li, Jianye Hao, Yuxin Peng
GraphMemix introduces a combinatorial‑optimization graph memory framework that organizes long‑term multimodal agent memory as query‑aware evidence forests. It constructs candidate graphs by expanding seed memories through schema and semantic relations, then decouples evidence utility from anchor‑conditioned relation verification to reduce redundancy, and finally optimizes a forest‑format memory context within a maximum evidence budget. Experiments on four benchmarks show significant accuracy gains and a new Pareto frontier between accuracy and lifecycle cost.
arXiv:2607. 29440v1 Announce Type: new Abstract: Long-term multimodal memory must support not only retrieving relevant information but also computing over observations accumulated across interactions.
By Zhoujin Tian, Yao Tian, Hao Zhang, Cheng Chen, Yakun Li, Lei Zhang, Xiaofang Zhou
EdgeMem is a new agent-memory method that preserves original interaction turns and organizes them using complementary content, temporal, and episodic cues via a multi‑anchor hypergraph. It performs lightweight local processing, returning source evidence directly and reserving LLM use only for final answer generation. Experiments on LoCoMo and LongMemEval‑S demonstrate strong retrieval and memory‑grounded question answering, with EdgeMem achieving the highest strict‑judge score among seven systems on LoCoMo while requiring no generative‑LLM calls for construction and retrieval.
By Zeyang Cui, Jiannong Cao, Zhiyuan Wen, Bo Yuan, Junlan Feng, Shengyuan Chen