The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
The paper introduces cognitive extensions for a dual‑process language agent, adding an Adaptive Memory Module (AMM) for episodic storage and a Self‑Reflection Module (SRM) for execution‑time validation. Implemented as feature‑flagged extensions, these modules allow controlled ablations on the ScienceWorld benchmark. Results show the full system outperforms baselines, with SRM providing the strongest individual improvement, indicating that execution‑time control is the key bottleneck while episodic memory aids once the runtime loop stabilizes.
arXiv:2609.07047v1 Announce Type: cross Abstract: Robotic manipulation often requires acting on information that is no longer visible, yet Vision-Language-Action policies are usually evaluated when t...
arXiv:2607. 17621v1 Announce Type: new Abstract: Existing self-evolving memory systems mainly improve agent memory based on textual outputs, such as task trajectories and reflections.
PREPING introduces a proposer-guided framework for constructing agent memory before encountering target tasks, using self-generated synthetic practice. The method employs a proposer memory to shape synthetic tasks, a solver to execute them, and a validator to filter useful trajectories, thereby avoiding redundancy and infeasibility. Experiments on AppWorld, BFCL v3, and MCP-Universe demonstrate that PREPING outperforms a no-memory baseline and rivals playbook-based approaches while reducing deployment costs by 2.99× and 2.23× respectively.
In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed.
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.