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Registers are implemented as dedicated fixed-position tokens whose continuous hidden states are trained to carry reasoning progress across generation chunks, which are especially effective for bounded code generation, where correct programs usually span several chunks.
StudentSim outperforms existing models, achieving a remarkable behavioral fidelity of 0.51 and guidance responsiveness of 0.91 in chess, setting a new standard for AI tutors.
AHR achieves state-of-the-art performance in text classification with a fraction of the parameters, revolutionizing how LLMs can adapt to limited data.
