cotomi Act: Learning to Automate Work by Watching You

📅 2026-05-04
📈 Citations: 0
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🤖 AI Summary
This work proposes a browser-based intelligent agent that learns to perform complex, multi-step tasks by passively observing user interactions, while continuously abstracting behavioral experience into reusable organizational knowledge—such as task boards and wikis—to enable human-agent collaborative editing and long-term evolution. Key methodological innovations include adaptive passive observation, verbal-diff–based history compression, a coarse-grained action space, an N-best action selection strategy, and a behavior-driven knowledge construction pipeline. Evaluated on the human-annotated subset of WebArena, the system achieves an 80.4% task success rate, surpassing the human baseline of 78.2%. Experimental results further demonstrate that accumulated knowledge significantly enhances long-term automation performance.
📝 Abstract
What if a browser agent could learn your work simply by watching you do it? We present cotomi Act, a browser-based computer-using agent that combines reliable multi-step task execution with persistent organizational knowledge learned from user behavior. For execution, an agent scaffold with adaptive lazy observation, verbal-diff-based history compression, coarse-grained actions, and test-time scaling via best-of-N action selection achieves 80.4% on the 179-task WebArena human-evaluation subset, exceeding the reported 78.2% human baseline. For organizational knowledge, a behavior-to-knowledge pipeline passively observes the user's browsing and progressively abstracts it into artifacts (task boards, wiki) exposed through a shared workspace editable by both user and agent. A controlled proxy evaluation confirms that task success improves as behavior-derived knowledge accumulates. In our live demonstration, attendees interact with the system in a real browser, issuing tasks and observing end-to-end autonomous execution and shared knowledge management.
Problem

Research questions and friction points this paper is trying to address.

browser agent
task automation
user behavior learning
organizational knowledge
autonomous execution
Innovation

Methods, ideas, or system contributions that make the work stand out.

browser agent
behavior-to-knowledge learning
adaptive observation
shared workspace
autonomous task execution
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