arXiv:2602.17588v3 Announce Type: replace Abstract: Despite rapid progress in autonomous web agents, human involvement remains essential for shaping preferences and correcting agent behavior as tasks unfold. However, current agentic systems lack a principled understanding of when and why humans intervene, often proceeding autonomously past critical decision points or requesting unnecessary confirmation. In this work, we introduce the task of modeling human intervention to support collaborative web task execution. We collect CowCorpus, a dataset of 400 real-user web navigation trajectories cont
The rapid advancement of autonomous web agents highlights a growing need to integrate human understanding for effective collaboration and error correction, addressing current limitations in agent design.
This work directly addresses a critical hurdle in AI agent development, moving beyond full autonomy to focus on human-agent collaboration and refining agent behavior through principled human intervention.
Previously, AI agent development often prioritized full autonomy; this research shifts focus to building agents that can intelligently understand and integrate human feedback at critical decision points.
- · AI developers
- · Companies implementing AI agents
- · Users of AI agent systems
- · Inefficient autonomous AI systems
- · Manual web task execution
AI agents will become more robust and user-aligned by learning when and why human input is crucial.
Improved human-agent collaboration could accelerate the deployment and adoption of complex AI-driven workflows across various industries.
This could lead to a new paradigm of human-in-the-loop AI systems that are more trusted and integrated into daily operations, rather than purely autonomous entities.
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Read at arXiv cs.CL