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GUI agents can now recover from errors far more effectively, thanks to a new benchmark and data synthesis method that specifically targets robustness.
Stop struggling with subjective LLM evaluation: RUBRIC-ARROW aligns models better by alternating between generating evaluation rubrics and judging against them, using only pairwise preferences.
