arXiv:2610.15642v1 Announce Type: new
Abstract: Under a limited context budget, a long-term agent must recall not merely memories that are individually relevant, but evidence that is collectively suf...
By Qi Feng, Jicong Fan
Long-term memory has become increasingly important for LLM agents that operate across extended interactions and evolving task contexts. Recent memory systems have made past experiences more persistent, compact, and retrievable, but retrieval alone does not ensure that a memory provides valid evidence for the current query.
JustMem is a new memory system for long‑term conversational AI that stores conversation history as compact atomic memories and adapts its access strategy to each query. It introduces two dimensions of memory access—discovery breadth and reading fidelity—implemented through LOOKUP for local evidence, COMPOSE for distributed evidence, and REPLAY for fidelity‑sensitive evidence. Experiments on LoCoMo and LongMemEval‑S show that JustMem outperforms competing memory systems in accuracy and recall while using fewer generative‑model tokens for memory construction and inference.
By Guanhua Chen, Yanting Wang, Wenjing Zhi, Lei Sha
MemoryLACE (MemLACE) is a lightweight memory framework that explicitly models the lifecycle of textual evidence—capturing sparse merge, supersession, and contradiction relations—while preserving atomic natural‑language memories and their provenance. Unlike traditional systems that retrieve memories independently, MemLACE reconstructs relation‑aware evidence units that expose current, historical, supporting, and conflicting evidence for downstream reasoning. In benchmark evaluations (BEAM and StructMemEval) using both open‑weight and proprietary LLM backbones, MemLACE achieves the highest overall performance among same‑backbone comparisons and reduces BEAM runtime by 66.6% compared to the strongest reflective‑memory baseline, Hindsight.
By Meriem Yacoubi, Pia Schmidt, Nenad Petrovic, Ahmed Frikha, Martin Kirchhoff, Alois Knoll
arXiv:2606. 17328v1 Announce Type: new Abstract: LLM agents increasingly maintain long-term memory of user facts across sessions.
By Xianxuan Long, Zhikai Chen, Shenglai Zeng, Shouren Wang, Kai Guo, Jiliang Tang
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