[ICML-2026] Official implementation of "SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience"
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Updated
Aug 7, 2025 - Python
[ICML-2026] Official implementation of "SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience"
Scalable pipeline for synthesizing verifiable RLVR training data for computer-use agents
PreAct: Computer-Using Agents that Get Faster on Repeated Tasks (arXiv:2606.17929). Compile successful runs into verified state-machine programs and replay them 8.5–13× faster with no per-step LLM calls.
Environment-Native Verified Search (ENVS): GUI agent post training pipeline that reaches higher accuracy at lower compute than online RL.
Evaluation of GPT-4o-mini on OSWorld desktop automation benchmark. Compares screenshot-only vs accessibility tree-enhanced approaches across 10 tasks (Chrome, LibreOffice, file ops, etc). Documents critical coordinate extraction failures and provides architectural recommendations for GUI agents.
Edge-optimized OpenCUA-7B computer-use agent evaluated on OSWorld, exploring systematic vLLM inference optimizations across CPU and GPU, including precision tuning, image history management, speculative decoding, and prefix caching.
GUI trajectory annotation: QEMU desktop VM + input-event tracing + screenshot pairing, with a web console for review & export.
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