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Ecdysis is proposed, an efficient and effective framework that distinguishes model-specific accommodation from harness-level repair and biases adaptation toward systematic harness deficiencies by identifying recurring cross-task failure patterns.
Transition-level supervision can dramatically enhance multimodal model performance, revealing that coherence between text and visuals is crucial for complex reasoning tasks.
Today's best AI agents can only solve 55% of real-world academic tasks that university students find challenging, revealing a significant gap between current AI capabilities and the demands of academic workflows.
