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The Agent-Ass kernels outperform the Full-Agent artifacts across the evaluated definitions, indicating that expert-provided optimization directions, high-quality references, and workload context remain critical for reliable AI-driven kernel optimization.
CogEvol, a family of models trained specifically for Learning Environment Generation: turning a course brief into a finished learning artifact (structured-JSON slides or self-contained interactive HTML pages) in a single pass, lowering the unit cost of AI-native education at scale.
This work proposes CVPO - Curriculum-guided Value-Variance Policy Optimization, a dynamic curriculum weighting method that adapts to question difficulty that achieves better performance and stronger exploration, enabling more accurate and robust reasoning in language models across various math tasks.
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