arXiv:2605.29218v1 Announce Type: new Abstract: Web agents, which couple language models with browsing and tool-use capabilities, show promise as open web assistants. Yet progress is increasingly limited by the lack of scalable, process-level supervision. Existing benchmarks are largely manually constructed, providing only coarse start-goal annotations without intermediate trajectories, while recent automatic generation efforts remain expensive, biased, and shallow. These limitations prevent reliable training and evaluation of agents that must generalize to realistic, multi-hop, cross-page tas
The proliferation of language models and rapid advancements in agentic capabilities necessitate more robust training and evaluation methodologies to overcome current limitations.
Scalable task generation for web agents addresses a critical bottleneck in the development of truly autonomous systems, enabling faster progress and broader application.
The ability to automatically generate complex, long-horizon tasks for web agents at scale will accelerate their development, moving them from rudimentary tools to sophisticated assistants.
- · AI agent developers
- · Web-based service providers
- · Automation platforms
- · AI infrastructure providers
- · Tasks requiring manual human supervision
- · Legacy automation solutions
- · Companies relying on simple, repetitive digital labor
More capable and reliable web agents emerge, expanding the scope of automated digital work.
Human-AI collaboration paradigms shift as agents handle increasingly complex digital workflows, freeing human workers for higher-level tasks.
The development of truly general-purpose web assistants could lead to significant reconfigurations of digital marketplaces and professional services.
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Read at arXiv cs.AI