arXiv:2607. 12893v1 Announce Type: new Abstract: Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions.
By Xixuan Hao, Zeyu Zhang, Zehao Lin, Yihang Sun, Ziliang Guo, Xichong Zhang, Yuxuan Liang, Feiyu Xiong, Zhiyu Li
The paper introduces a scale‑conditioned evaluation protocol for agent memory that measures how usable evidence degrades as irrelevant sessions accumulate. It logs memory trajectories and reports four diagnostics—budget‑compliant reliability, tail memory‑call burden, failure‑regime decomposition, and the usable‑scale boundary. Experiments on LongMemEval and LoCoMo show that reliability loss varies across memory interfaces and agents, highlighting that scalable‑memory claims must be conditioned on agent, interface, scale range, and interaction budget.
By Jiaqi Shao, Yiyi Lu, Yunzhen Zhang, Bing Luo
The paper introduces D$^2$ACCI, a dual-loop diagnostic protocol designed to improve evidence-preserving memory in large language model agents. It provides a structured framework that uses paired evidence, protected-slice monitoring, and trace-level localizability to decide whether to promote, flag, or reject memory interventions. The authors also present DCR, a metric for measuring failure localizability, and D$^2$ACCI‑Eval, a reusable artifact for gate replay, demonstrating significant performance gains on three public benchmarks and highlighting the importance of traceable, statistically grounded diagnostics.
By Xule Liu, Yijun Liu, Chao Li, Shao Kun
arXiv:2608.21755v2 Announce Type: replace
Abstract: Long-horizon agents require more than retrieval over stored conversations: they must preserve source evidence, revise outdated knowledge, and disti...
By Yu Qian, Hong Miao, Boyang Guo, Tingyi Jiang, Shan Zhao, Tianxing Le, Lintian Li, Meng Liu
arXiv:2609. 04875v1 Announce Type: cross Abstract: Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memory, pending tool plans, and, under every serving API, a KV cache.
By Chao Yao, Yangbo Wei, Zhen Huang, Junhong Qian, Chenle Chen, Shaoqiang Lu, Chen Wu, Lei He
The paper introduces D$^2$ACCI, a dual-loop diagnostic protocol designed to improve evidence-preserving memory in large language model agents. It provides stage-level diagnostic traces, protected-slice monitoring, and a graded observability metric (DCR) to localize failures within the memory pipeline. Applied to MemStack and evaluated on three benchmarks, the protocol achieves high accuracy and demonstrates statistically significant gains from specific memory interventions.