GUI-HARVEST analyzes repeated attempts at computer-use tasks.
Evidence-driven harness edits improve frozen GUI agents across benchmarks
GUI-HARVEST diagnoses visual execution failures, converts recurring patterns into runtime edits, and validates whether those edits produce the predicted behavior.
Top University
Geyi Yang · Zikun Qu · Xiang Li · Zhiyong Wang · Min Zhang · Shipei Zeng · +1 more
The Chinese University of Hong Kong, Shenzhen · Tianjin University · Harbin Institute of Technology (Shenzhen) · East China Normal University · Shenzhen Research Institute of Big Data
Research Digest··2 min read
Yang et al.
Why this paper
From The Chinese University of Hong Kong, Shenzhen and 4 others · Released code
In one line
GUI-HARVEST automatically improves GUI agent harnesses by diagnosing failures from repeated executions and generating reusable edits, boosting performance across models.
What it released
Code
What we could check
- ✓Code link in the paper (github.com)
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ✓Reports numbers on named benchmarks (2 benchmarks)
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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