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Prescriptive scaling laws: estimate stable capability boundaries vs. pretraining compute, track temporal shifts, and release the Proteus 2k evaluation dataset.

We propose adaptive, state-model-free bandit algorithms for latent-state (confounded, non-stationary) environments.

Sharp minimax theory for structure-agnostic estimation of general linear functionals, including optimality of first-order debiasing and doubly robust estimators.

We propose a causal representation learning framework to understand the hierarchical structure of language model capabilities, revealing causal relationships between general problem-solving, instruction-following, and mathematical reasoning abilities.