JAMEL jointly learns agent memory and exploration via novelty-driven interaction, generalizing to unseen environments while outperforming open baselines and reducing token use.
Actio- nengine: From reactive to programmatic gui agents via state machine memory
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DRIVE disentangles reasoning and interaction skills for web agents via dual-level modeling and scene-aware coordination, reaching 52.8% success on WebArena tasks.
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment interventions.
citing papers explorer
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Joint Agent Memory and Exploration Learning via Novelty Signals
JAMEL jointly learns agent memory and exploration via novelty-driven interaction, generalizing to unseen environments while outperforming open baselines and reducing token use.
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DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
DRIVE disentangles reasoning and interaction skills for web agents via dual-level modeling and scene-aware coordination, reaching 52.8% success on WebArena tasks.
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Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment interventions.