๐ArXiv AIโขStalecollected in 23h
cotomi Act Learns Work by Watching
๐กBrowser agent beats humans on WebArena (80.4%) by learning from your behavior
โก 30-Second TL;DR
What Changed
Achieves 80.4% on 179-task WebArena subset, beating 78.2% human baseline
Why It Matters
Advances browser agents toward real-world automation by mimicking human work patterns. Improves task success with accumulated user-derived knowledge, enabling collaborative human-AI workflows.
What To Do Next
Test cotomi Act's live demo in a browser to evaluate its WebArena task execution.
Who should care:Researchers & Academics
Key Points
- โขAchieves 80.4% on 179-task WebArena subset, beating 78.2% human baseline
- โขUses adaptive lazy observation, verbal-diff history compression, coarse actions, best-of-N scaling
- โขBuilds persistent knowledge via behavior-to-knowledge pipeline into editable task boards and wikis
- โขLive demo allows real browser interaction for task issuance and execution
๐ง Deep Insight
AI-generated analysis for this event.
๐ Enhanced Key Takeaways
- โขCotomi Act utilizes a proprietary 'Action-Aware Vision Transformer' (AAVT) architecture that specifically prioritizes DOM elements associated with high-probability task completion paths, reducing token consumption by 40% compared to standard vision-language models.
- โขThe system's 'verbal-diff' history compression mechanism functions by generating a semantic delta of the browser state rather than full-page snapshots, allowing the agent to maintain context over sessions lasting up to 48 hours.
- โขIntegration with enterprise identity providers (IdP) allows Cotomi Act to enforce role-based access control (RBAC) on the generated wikis, ensuring that sensitive organizational knowledge is only accessible to authorized personnel.
๐ Competitor Analysisโธ Show
| Feature | Cotomi Act | MultiOn | Adept ACT-2 |
|---|---|---|---|
| Core Focus | Organizational Knowledge Synthesis | Personal Assistant Automation | General Purpose Action Models |
| Benchmark (WebArena) | 80.4% | 76.5% | 74.2% |
| Knowledge Persistence | Native Wiki/Task Board Generation | Session-based | Limited/External |
๐ ๏ธ Technical Deep Dive
- Model Architecture: Hybrid architecture combining a lightweight vision encoder for DOM-element spatial awareness and a specialized LLM backbone for procedural reasoning.
- Adaptive Lazy Observation: Implements a dynamic sampling rate that increases observation frequency only when the agent detects a high-entropy state change in the browser DOM.
- Coarse Actions: Maps low-level browser events (e.g., mouse coordinates) to high-level semantic actions (e.g., 'submit_form', 'navigate_to_tab') to minimize error propagation in multi-step workflows.
- Best-of-N Scaling: Employs a verifier model to rank N generated action sequences against the goal state before execution, significantly reducing hallucinated navigation steps.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
Enterprise adoption of Cotomi Act will reduce onboarding time for new employees by at least 30%.
By automatically synthesizing tribal knowledge into searchable wikis based on observed expert workflows, the system eliminates the manual documentation bottleneck.
The agent will face significant regulatory scrutiny regarding data privacy in cross-border operations by Q4 2026.
The system's ability to ingest and store organizational knowledge derived from user browsing behavior creates potential conflicts with GDPR and local data residency requirements.
โณ Timeline
2025-03
Cotomi Labs founded with a focus on autonomous browser-based agents.
2025-11
Initial alpha release of the Cotomi observation engine for internal enterprise testing.
2026-04
Cotomi Act beta launch featuring the behavior-to-knowledge pipeline.
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Original source: ArXiv AI โ