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Reversible action cycles enable self-verification in long-horizon planning, cutting state returning drift by 44% and boosting accuracy nearly 4x.
Forget end-to-end training for complex vision tasks; VisHarness shows that a lightweight agent can outperform task-specific models by learning to orchestrate a diverse set of pre-trained visual experts.
Forget tedious, brittle automation scripts: RL-powered GUI agents are showing signs of "System 2" reasoning without explicit supervision, hinting at a future of truly intelligent digital inhabitants.
