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This paper proves that task-relevant latent representations can be identified from generalist models in a fully nonparametric setting without interventions or parametric constraints, achieving a hierarchical identifiability guarantee across time steps and within each step.
This paper establishes nonparametric identifiability guarantees for extracting task-relevant representations from generalist models, proving that task structure is identifiable across time steps and latent representations are identifiable within each step under sparsity regularization.