DeltaReplay is a step‑level memory reuse framework for mobile GUI agents that decides how to use stored execution trajectories without modifying them. It stores trajectories as paths in a transition graph, separating each action into a task‑independent operation and task‑specific parameters, and compares each recorded step with the new task and current screen to decide whether to follow, adapt, or ignore it. Experiments on AndroidWorld and SPA‑Bench show that DeltaReplay can increase task success rates by up to 10.3 and 25.0 percentage points over a base agent with the same backbone.
By Yudong Bai, Yihong Chen, Quanming Yao, Yaqing Wang
The paper introduces TAG-Nav, a task‑agnostic graph navigation framework that augments mobile GUI agents with reusable destination memory, enabling offline exploration to be distilled into compact records of app functions, target pages, and waypoint routes. It also presents DestBench, a navigation‑only evaluation protocol that isolates route finding from downstream task execution. Experiments show that graph‑derived memory improves navigation success rates for several large‑language‑model agents, demonstrating that destination coverage and route cleanliness are key factors in the value of graph memory.
By Adamo Orsini, Seyed Mehdi Ayyoubzadeh, Sadman Rakib Pinon, Hannuo Zhang, Koosha Gholipour Baradari, Yuanhao Yu, Xinxin Zuo, Yang Wang
The paper introduces REM (Reusable Experience Memory), a framework that manages the lifecycle of experience for large language model agents performing long-horizon coding tasks. REM consolidates completed trajectories into compact, phase-structured documents, injects them at task start, and evicts them after a single exposure, making the memory system responsive to the agent’s needs. Experiments on repository-level bug fixing show REM outperforms state‑of‑the‑art memory systems in retrieval accuracy and task resolution while reducing cost.
By Xinyi Lisa Chen, Pedro Herrero-Vidal, Xiaohan Chen, Xiaojing Yu, Cris Liu, Bingqing Song, Bin Xu, Suleiman A. Khan, You-Lin Chen
arXiv:2607. 10608v1 Announce Type: new Abstract: Memory is becoming a core component of long-horizon AI agents, allowing agents to reuse past experience when operating web browsers, software tools, and other interactive environments.
By Yixiong Chen, Xinyi Bai, Alan Yuille
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
The paper introduces a framework that selectively reuses parts of previously executed mobile GUI control traces to reduce LLM inference overhead. It identifies six categories of variation that can affect trace reuse and shows that individual transitions can still be valid when checked against the current task context and UI state. Using graph-text templates, the system achieves roughly 80% fewer LLM tokens and up to 2‑fold speedups on Android and HarmonyOS devices.
By Bingyu Bai, Cheng Zhang, Erhu Feng
arXiv:2608. 12847v1 Announce Type: new Abstract: Retrieval can identify a past trajectory that may matter, yet it does not specify how an acting agent should use that trajectory after users, entities, constraints, or environment state have changed.
By Yifei Li, Heng Wang, Lingling Zhang, Muye Huang, Xinyu Zhang, Jiashuai Liu, Hang Yan, Rongman Xu
arXiv:2609.35808v1 Announce Type: cross
Abstract: Experience reuse can reduce repeated exploration in embodied agents, but a trajectory that succeeded previously may be unsuitable for the current exe...
By Quanquan Li, Hongbo Zhang, Yihe Chi, Liuyang Song, Jingyu Li, Yuxiang Huang, Hongzhen Zhang, Guitao Cao
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
MemCo introduces a memory‑centric collaboration framework that equips large language model agents with both local and global memory spaces. Local memory preserves environment‑specific details, while global memory captures transferable workflows from local trajectories. During online interaction, MemCo selectively routes relevant memories based on the agent’s state and decision phase, enabling reuse of other agents’ experiences without over‑generalizing environment‑specific information.
By Xinting Liao, Siyan Liu, Rabab K. Ward, Holger R. Roth, Xiaoxiao Li
arXiv:2610.20022v2 Announce Type: replace
Abstract: Memory systems for persistent personal agents must move beyond isolated memories, maintaining both personal memory about whom they serve and proced...
By Maosheng Qin, Qi Zhang, Mingxuan Xia, Jinglei Chen, Jiexiang Wang, Sheng Guo, Cheng Peng, Haobo Wang
Memory systems for persistent personal agents must move beyond isolated memories, maintaining both personal memory about whom they serve and procedural memory about how to act. Crucially, in harness-b...