DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
arXiv:2605.23939v1 Announce Type: cross Abstract: Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowledge types differ fundamentally: reasoning knowledge (e.g., booking a flight requires first searching for routes) is abstract and transferable across websites, while interaction knowledge (e.g., clicking the Search button at a specific coordinate on Site A) depends heavily on page-specific contexts. Existing methods store experiences uniformly. This creates a dilemma: a
The proliferation of complex web interfaces necessitates more adaptive and intelligent autonomous agents capable of nuanced interaction and reasoning under continuous learning paradigms.
This research advances the core capabilities of autonomous AI agents by addressing the fundamental challenge of integrating abstract reasoning with concrete, context-dependent interaction skills, leading to more robust and versatile systems.
Current AI agent architectures that struggle with uniformly managing diverse knowledge types will be superseded by more sophisticated models capable of segregating and integrating reasoning and interaction knowledge effectively.
- · AI Agent development platforms
- · Companies seeking task automation
- · Web automation software providers
- · Monolithic AI agent architectures
- · Manual web-based task forces
Autonomous web agents become significantly more reliable and capable across a wider array of online tasks.
The cost of digital services and online operations is reduced as agents can handle more complex, multi-step processes unattended.
This could accelerate the development of general-purpose AI agents that learn and adapt continuously in dynamic digital environments, potentially collapsing many white-collar workflows.
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