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Native desktop automation requires seamless GUI and code integration, a feat previously unachievable with prompt-level tool composition, but OS-Helix now unifies these into a single action space.
Achieving top-tier performance in complex professional tasks with a model significantly smaller than its competitors reveals a new frontier in agentic intelligence.
Decomposing GUI agent trajectories into verifiable milestones and auditing the evidence chain yields a 10% boost in RL training performance, outperforming single-judge reward systems.