arXiv:2610.17763v1 Announce Type: new
Abstract: Long-term memory supports agents across sessions. Repeated retrieval and write-back can let one historical claim occupy multiple retrieval positions an...
By Yufeng Li, Sheng Yao, Peng Zeng, Dezhang Kong, Shi Lin, Changting Lin, Xun Wang, Meng Han
arXiv:2609.25054v1 Announce Type: new
Abstract: For a long-horizon LLM agent, the memory question is not what was once recorded but what \emph{currently holds}. Most designs answer it only indirectly...
By Bowen Qin, Yao Lu
The paper investigates how memory systems can answer a current query correctly yet fail to retain distinctions needed for later updates. Using a paired‑history audit, the authors evaluate 24 history pairs across six synthetic mechanisms and two model backends, achieving perfect reveal accuracy on DeepSeek and high accuracy on GLM. Record‑level audits reveal specific failures in structured reveal memories and frontier late‑reference adequacy, and the authors test a label‑equivariant repair that only partially restores correctness.
By Guangzhe Zhang
Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators. However, memory is not always beneficial: retrieved memories often induce a critical issue of sycophancy, causing agents to over-align with the user at the cost of factual accuracy or objective reasoning.
arXiv:2607. 01071v1 Announce Type: cross Abstract: Memory has emerged as a cornerstone of modern LLM-based agents, supporting their evolution from single-turn assistants to long-term collaborators.
By Zhishang Xiang, Zerui Chen, Yunbo Tang, Zhimin Wei, Ruqin Ning, Yujie Lin, Qinggang Zhang, Jinsong Su
The paper introduces two inference‑time designs—factor‑compiled admission (FC) and permission‑semantic admission (PS)—to govern persistent memory in large language model agents without retraining. FC evaluates entire memory entries, while PS decomposes entries into typed units, both using deterministic policies to decide what recalled information enters the working context and how it is expressed. Experiments on a development suite and an external benchmark show that FC and PS reduce failure rates and cross‑domain leakage compared to verbatim injection, though they increase personalization failures and do not meet all preregistered criteria.
By Chang Liu, Deliang Ding