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
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.
Reglos is a training‑free personal memory agent that continuously updates an append‑only record of user interactions, allowing newer preferences to override older ones and enabling selective forgetting of facts. Its memory harness evolves through recursive self‑improvement: a coding agent generates patches from failure traces, a critic from a second model family screens them, and a fixed evaluator decides which patches to accept. Using the same backbone across systems, Reglos surpasses state‑of‑the‑art memory agents such as Mem0, MemOS, and EverOS, achieving 61.3 % and 84.0 % on PersonaMem‑v2 and LongMemEval‑S with Qwen3.8‑27B, and 57.5 % and 87.2 % with GPT‑5.6 Luna.
By Sen Yang, Jiaduo Lin, Zehong Huang, Wen Liu
arXiv:2507. 05257v4 Announce Type: replace-cross Abstract: Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while another critical component-memory, encompassing how agents memorize, update, and retrieve long-term information-is under-evaluated due to the lack of benchmarks.
By Yuanzhe Hu, Yu Wang, Julian McAuley
arXiv:2605.12978v2 Announce Type: replace
Abstract: Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolid...
By Dylan Zhang, Yanshan Lin, Zhengkun Wu, Yihang Sun, Bingxuan Li, Dianqi Li, Hao Peng
Autonomous computer-use agents are increasingly applied to long-horizon tasks requiring coordinated application calls, persistent state tracking, and verifier-sensitive writes, yet they remain prone t...