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Evolving coding problems can restore meaningful evaluation metrics for frontier models, revealing their true capabilities and enabling self-improvement.
RAG's reputation for being ineffective in reasoning tasks is shattered by showing that retrieving the right data – intermediate "thinking traces" – unlocks substantial performance gains, even for state-of-the-art models.
