EMMA is a framework that treats the configuration of a multi‑agent system—controller routing, specialist prompts, and procedural skills—as structured episodic memory. It explicitly assigns ownership and limits updates through a reflector and backbone‑independent guards, ensuring edits are scoped, incremental, and bounded. Experiments on KramaBench, AppWorld, and BBEH show EMMA outperforms four baselines, achieving up to 32.5% higher performance and reducing training executions by 17–38%.
By Ruijia Niu, Kaiwen Liu, Rose Yu, Kristina Klinkner
The paper investigates how large language model (LLM) agents can improve performance by storing and reusing experience in external memory. It identifies a problem called error reproduction, where mistakes in stored abstractions propagate through cycles of abstraction, execution, and re-abstraction, leading to persistent errors. To mitigate this, the authors propose Anti-Persistence, a memory design that retains raw trajectories and outcomes but reconstructs task-specific guidance on demand, discarding it after use. Experiments across multiple benchmarks show that Anti-Persistence yields higher held‑out success rates and lower LLM‑call costs compared to other memory designs, supporting the idea of separating experience accumulation from abstraction persistence.
By Ruhan Wang, Kishan Panaganti, Dongruo Zhou
arXiv:2607. 10059v1 Announce Type: new Abstract: Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain.
By Xun Liu, Yi Evie Zhang, Vira Kasprova, Parisa Rabbani, Pardis Sadat Zahraei, Tianyu Zhang, Ali Ebrahimpour-Boroojeny, Varun Chandrasekaran
MemoryArena is a new evaluation gym that benchmarks agent memory in interdependent multi‑session tasks. Unlike prior benchmarks that test memorization or single‑session action in isolation, MemoryArena requires agents to acquire memory while interacting with the environment and then use that memory to guide future decisions across a range of tasks such as web navigation, planning, information search, and formal reasoning. The benchmark reveals that agents excelling on existing long‑context memory tests perform poorly here, highlighting a gap in current memory evaluation methods.
By Zexue He, Yu Wang, Churan Zhi, Yuanzhe Hu, Tzu-Ping Chen, Lang Yin, Ze Chen, Tong Arthur Wu, Siru Ouyang, Zihan Wang, Jiaxin Pei, Julian McAuley, Yejin Choi, Alex Pentland
arXiv:2609. 04875v1 Announce Type: cross Abstract: Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memory, pending tool plans, and, under every serving API, a KV cache.
By Chao Yao, Yangbo Wei, Zhen Huang, Junhong Qian, Chenle Chen, Shaoqiang Lu, Chen Wu, Lei He
The paper introduces MemPABench, an interactive environment designed to evaluate how personal agents remember and act on user interaction preferences. The benchmark features long‑horizon tasks where agents must decide when to clarify, confirm, or act autonomously, using data from four simulated users who provided 99 sessions over four months and five preference changes each. Experiments across four memory systems and LLM backbones show that current memory approaches offer limited improvement in preference selection, with retrieval‑based methods performing no better than no memory and file‑based memory sometimes applying preferences in irrelevant contexts.
By Jialin Li, Zhenhao Chen, Hanjun Luo, Hanan Salam
arXiv:2607.10526v5 Announce Type: replace
Abstract: Self-improving personal agents write profiles, memories, and reusable skills that carry over from one chat to the next. Prior works ask whether a m...
By Xutao Mao, Liangjie Zhao, Leyao Wang, Rui Qian, Qiang Huang, Wentao Wang, Bo Han, Xiang Zheng, Cong Wang
AdaMem introduces adaptive memory policies that allow personalized agents to decide what information to write into long‑term memory based on user preferences for each interaction context. Each policy is updated from periodic feedback and controls subsequent memory writing, aiming to improve relevance and reduce unnecessary memory persistence. In experiments on AdaMem‑Bench, AdaMem raises QA accuracy from 80.0% to 84.35% while cutting persistent memory by 9.27%, though models still struggle to execute policies reliably.
By Xingyu Chen, Rui Wang, Zhaopeng Tu, Liefeng Bo
The paper introduces the Agent-Editing World Model (AEWM), a new approach that models how reasoning and actions influence future task progress instead of simulating tool responses. AEWM includes an Action Judge that classifies decisions as Critical, Exploratory, or Noisy, and a State Revision mechanism that edits noisy reasoning–action continuations from the same observed history. The integrated system, EditAct, directly updates the underlying state during real execution, leading to significant performance gains across multiple benchmarks and agent backbones.
By Shuang Sun, Guoxin Chen, Fanzhe Meng, Jia Deng, Huatong Song, Jinhao Jiang, Wayne Xin Zhao, Hongteng Xu, Ji-Rong Wen
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:2610.18684v1 Announce Type: new
Abstract: Personal-agent memory has largely focused on learning what users know, prefer, and want. However, knowing the user does not necessarily mean knowing wh...
By Jiajun Chen, Xinyue Chen, Yicheng Jiang, Hua Shen
arXiv:2610.17659v1 Announce Type: new
Abstract: Large language model (LLM) agents have shown strong capabilities in interactive problem solving, tool use, and long-horizon task execution. However, th...
By Junkai Lin, Ge Xu, Zheng Xu, Junkai Chen, Xiangyuan Lan