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LLM-generated patches are not only larger but also more complex than human-written ones, and RECAP offers a solution that significantly reduces this verbosity without sacrificing effectiveness.
ST-Omni-R1 not only excels in sound-event recognition but also sets a new standard for spatial audio reasoning, achieving nearly double the accuracy of existing models.
Early failure prediction can save up to 20.4% of execution tokens while improving resolution rates in software engineering tasks, transforming how agents handle long trajectories.
AutoGlue achieves a 58.7% improvement in API F1 scores, demonstrating that LLMs can seamlessly translate natural-language requirements into executable code.
