The paper introduces TriMem, a memory system for lifelong LLM agents that stores raw dialogue segments, extracted atomic facts, and synthesized profiles to preserve detail, enable efficient retrieval, and support deep reasoning. It uses TextGrad-based prompt optimization to iteratively refine extraction and profiling prompts without updating model parameters. Experiments on LoCoMo and PerLTQA show TriMem outperforms strong memory baselines across multiple LLM backbones.
By Jingwei Sun, Jianing Zhu, Jiangchao Yao, Tongliang Liu, Bo Han
arXiv:2605. 29640v3 Announce Type: replace Abstract: Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-term interactions.
By Jiajie Fu, Junwen Chen, Mengzhao Wang, Aoxiang He, Maojia Sheng, Xiangyu Ke, Yifan Zhu, Yunjun Gao
ROAM is a relation‑guided framework for managing atomic memories in long‑term language‑model agents. It classifies atom pairs as independent, equivalent, subsuming, or conflicting, then organizes observations into Primary and Evidence roles, fusing complementary details into compact views. Only Primary views are retrieved for answering, which reduces redundancy and improves answer accuracy by up to 29.8 percentage points across models and settings.
By Jianjie Zheng, Peng Lai, Sijie Cheng, Jiehui Zhao, Lei Yang, Guanhua Chen
Long-term language-model agents rely on external memory across interactions. Atomic memories are particularly useful: their fine-grained semantic boundaries enable precise retrieval and direct compari...
The paper introduces StructMemEval, a benchmark designed to assess how well large language model (LLM) agents can organize their long‑term memory rather than merely recall facts. It compiles tasks that humans typically solve by structuring knowledge—such as transaction ledgers, to‑do lists, and trees—and evaluates agents on these. Experiments show that simple retrieval‑augmented LLMs struggle with such organization tasks, while memory‑augmented agents perform better when explicitly prompted to structure their memory, yet many modern LLMs still fail to recognize memory structures without prompting.
By Alina Shutova, Alexandra Olenina, Ivan Vinogradov, Anton Sinitsin
arXiv:2608. 03463v1 Announce Type: new Abstract: Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history.
By Yuxin Liao, Le Wu, Min Hou, Hao Liu, Han Wu, Zishu Wang