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The frontier of AI is shifting from raw model performance to contextual intelligence, as seen in GPT-5.6 Sol's autonomous post-training and the focus on value generalization for alignment. This evolution is accompanied by new tools for understanding AI, like mapping reasoning levels to task complexity and analyzing autoencoder robustness. As a result, the strategy for managing risk is changing, with a move away from pure capability measurement toward multimodal provenance and contextual safeguards. These developments signal a maturing field where how an AI thinks and operates within its context is becoming as important as its underlying power.