As memories accumulate across tasks and sessions, the performance of long-term LLM agents depends jointly on query-specific retrieval and continual memory refinement. However, existing methods typically optimize either memory access, through iterative query refinement or adaptive retrieval policies, or memory evolution such as structural update.
arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.
By Eric Hanchen Jiang, Zhi Zhang, Yuchen Wu, Levina Li, Dong Liu, Xiao Liang, Rui Sun, Yubei Li, Edward Sun, Haozheng Luo, Zhaolu Kang, Aylin Caliskan, Kai-Wei Chang, Ying Nian Wu
Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics.
arXiv:2603. 27803v2 Announce Type: replace Abstract: We provide a distributed online algorithm for multi-agent submodular maximization under communication delays.
By Zirui Xu, Vasileios Tzoumas
arXiv:2608. 09565v1 Announce Type: cross Abstract: Optimization theory is a widely used tool for intelligent decision-making.
By Muhammad Faraz Ul Abrar, Nicol\`o Michelusi, Erik G. Larsson
arXiv:2607. 01665v1 Announce Type: new Abstract: Decentralized online convex optimization (D-OCO) is a popular framework for distributed applications with streaming data.
By Hao Zhou, Xiaoyu Wang, Chang Yao, Mingli Song, Yuanyu Wan