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Skill augmentation can elevate CLI agent performance beyond GUI counterparts, revealing that the true challenge lies in skill coverage rather than model capability.
Rubric-Conditioned Self-Distillation transforms vague scalar rewards into precise, actionable feedback, leading to significant improvements in reasoning model performance.
Current AI agents excel in structured tasks but falter at generating novel insights and tackling open-ended scientific challenges.