arXiv:2610.33274v1 Announce Type: new
Abstract: Large language model (LLM) agents work in scenarios with very different memory needs. A question about past conversations needs the exact facts it name...
By Wujiang Xu, Jiaojiao Han, Xi Zhu, Qizheng Zhang, Kaiqu Liang, Yutong Yin, Kai Mei, Dimitris N. Metaxas
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
APDMem is a hierarchical long‑term memory system for personalized LLM assistants that uses progressive disclosure to retrieve conversation history. It organizes memory into four layers—thematic summaries, personalized key facts, turn‑level evidence notes, and raw messages—and a controller reads from the top layer, drilling down only when necessary. This approach balances cost and fidelity, allowing simple queries to finish early while deeper inspection is triggered for complex or exact‑evidence requests, and a note synthesizer structures retrieved evidence before answer generation. Experiments on LongMemEval show APDMem performs strongly while accessing only 8% of the total conversations.
By Chin-Lun Fu, Anagha Kulkarni, Hong Ni, Behrouz Madahian
Long‑horizon LLM agents use context compaction to replace an overflowing interaction history with a natural‑language summary. The paper shows that these summaries mix factual environment data with the compressor’s reasoning, making it hard for the agent to distinguish the two. The authors introduce Source‑Judged Compaction (SJC), a training‑free post‑processing step that scores each summary entry for a source in the environment or previous summary and removes unsupported entries, improving cross‑run reliability by 2.1%–8.9% on AppWorld and OfficeBench without increasing token usage.
By Guanghui Min, Chen Chen
M-Plan is a closed‑loop multi‑agent system that combines long‑term and short‑term memory to improve large language model agents on long‑horizon, multi‑turn tasks. It uses task‑specific milestones and local hints to generate global‑local guidance, forming short‑term memory during execution, and then optimizes and stores relevant information in long‑term memory for future use. Experiments on ALFWorld, Webshop, and PDDL show that M‑Plan outperforms baseline methods in both accuracy and efficiency.
By Ziyang Liu, Jiakang Yuan, Jiayuan Fan, Tao Chen
The paper introduces MERIT, a benchmark that evaluates the marginal benefit of long‑term memory for tool‑using large language model agents while explicitly accounting for cost. MERIT provides episodic tool‑use tasks across three domains, verifies dependence on earlier‑episode facts, and measures memory operations in tokens and dollars. Experiments on GPT‑4.1‑mini, Claude Haiku 4.5, and Claude Sonnet 5 show that memory can significantly improve task success, but its utility varies widely across models and memory implementations, and full replay is rarely cost‑effective.
By Shweta Mishra, Shashank Mishra