The paper introduces an action‑conditioned bisimulation rule for merging states in a GUI agent’s memory, replacing observation‑based similarity with a requirement that shared actions produce identical outcomes and successor blocks. This rule is applied to the empirical predictive state graph built by a frozen agent during interaction. Experiments on MiniWoB++ show that the new merge rule improves success rates compared to a memoryless baseline, while controls that omit action conditioning or use the previous successor‑representation merge show no improvement.
arXiv:2608. 02508v1 Announce Type: new Abstract: Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges.
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...
arXiv:2607. 08716v1 Announce Type: new Abstract: In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act.
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.
arXiv:2610.23372v1 Announce Type: cross Abstract: When we say that a robot uses memory, does that memory actually play a role? Success-rate ablations alone cannot establish the use of task-relevant f...
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...
MemBodied introduces a fixed‑size episodic memory for Vision‑Language‑Action models, comprising an associative state that tracks interactions across policy calls and an episode anchor that stores a compact representation of the initial scene. By conditioning action generation on these memory components instead of raw past observations, MemBodied reduces context bloat and inference latency. In five memory‑dependent RMBench tasks, it outperforms stateless and vanilla recurrent policies by significant margins, and achieves a 90.6% success rate on the LIBERO‑Long suite, improving over the baseline by 5.4%.
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.
arXiv:2603. 24576v2 Announce Type: replace-cross Abstract: Robots often observe information that determines a future action long before that action is executed.
DeltaReplay introduces a step-level memory reuse framework for mobile GUI agents that selectively reuses parts of stored execution trajectories without modifying them. By representing trajectories as paths in a transition graph and separating actions into task-independent operations and task-specific parameters, the system 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 this selective reuse can boost task success rates by up to 10.3 and 25.0 percentage points over a baseline agent.
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.
arXiv:2609.37359v1 Announce Type: cross Abstract: Coding agents can now write, run, and debug programs with little human help. Robot tasks, however, are usually specified by a sentence that leaves ou...