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An anytime solver that achieves 99.90% accuracy on Sudoku-Extreme by leveraging persistent hidden states and maximum corruption during inference upends traditional reasoning models.
Foundation model agents can achieve stable cooperation in social dilemmas by inferring behavioral similarities, defying classical game theory's expectations of mutual defection.
Normalizing error signals, not just activations, is the key to unlocking the benefits of inhibition-mediated normalization for learning in neural networks.
