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Personalized AI agents can achieve up to 20.9% better task success by learning from user feedback in real-time, reshaping the landscape of human-AI collaboration.
Decomposer achieves superior MIDI reconstruction fidelity and code readability compared to existing models, transforming how we approach symbolic music decompilation.
Even top-performing AI models struggle with PowerPoint tasks, achieving only 45% success rates despite a robust evaluation framework that rewards nuanced performance.
Forget specialized tools: a standard Unix terminal and clever RL are all you need to beat much larger LLMs at code search.