arXiv — cs.AI preprintsInternational2 October 2026
GUI-HARVEST: Self-Improving GUI Agents through Evidence-Driven Harness Evolution
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.00948v1 Announce Type: cross Abstract: The executable harness surrounding a GUI model determines how observations are assembled, actions are executed, and verification, recovery, and termination are controlled. Compared with harness optimization for non-GUI agents, automatically optimizing this harness poses three coupled challenges: reconciling model intent with observed visual effects, diagnosing failures under variable execution outcomes, and identifying recurrent failure patterns across tasks and translating them into reusable runtime changes. We introduce GUI-HARVEST, an automa
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Heavy-Tailed Memory Traces in Long-Horizon Language Agents2 October 2026
- When Do Causal World Models Help Modular LLM Agents2 October 2026
- From Proposal to Verified Effect: Praxa, an Evidence-Bound Harness for Governed AI Agent Execution2 October 2026
- What Do Rationales Communicate? A Message-Intervention Study in Role-Specialized QA2 October 2026
- Measuring the Microtask Eligibility Gap: When Is an Off-the-Shelf SLM Enough for an Agent Harness?2 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.