From Generalist to Specialist Representation

Hugging Face Daily Papers Papers
generalist-model specialist-representation identifiability nonparametric latent-representation sparsity-regularization task-structure

Summary

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.

Given a generalist model, learning a task-relevant specialist representation is fundamental for downstream applications. Identifiability, the asymptotic guarantee of recovering the ground-truth representation, is critical because it sets the ultimate limit of any model, even with infinite data and computation. We study this problem in a completely nonparametric setting, without relying on interventions, parametric forms, or structural constraints. We first prove that the structure between time steps and tasks is identifiable in a fully unsupervised manner, even when sequences lack strict temporal dependence and may exhibit disconnections, and task assignments can follow arbitrarily complex and interleaving structures. We then prove that, within each time step, the task-relevant latent representation can be disentangled from the irrelevant part under a simple sparsity regularization, without any additional information or parametric constraints. Together, these results establish a hierarchical foundation: task structure is identifiable across time steps, and task-relevant latent representations are identifiable within each step. To our knowledge, each result provides a first general nonparametric identifiability guarantee, and together they mark a step toward provably moving from generalist to specialist models.
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Cached at: 05/14/26, 08:20 PM

Paper page - From Generalist to Specialist Representation

Source: https://huggingface.co/papers/2605.12733

Abstract

Nonparametric identifiability results establish foundational guarantees for extracting task-relevant representations from generalist models without parametric assumptions or interventions.

Given ageneralist model, learning a task-relevantspecialist representationis fundamental for downstream applications.Identifiability, the asymptotic guarantee of recovering the ground-truth representation, is critical because it sets the ultimate limit of any model, even with infinite data and computation. We study this problem in a completelynonparametric setting, without relying on interventions, parametric forms, or structural constraints. We first prove that the structure between time steps and tasks is identifiable in a fully unsupervised manner, even when sequences lack stricttemporal dependenceand may exhibit disconnections, and task assignments can follow arbitrarily complex and interleaving structures. We then prove that, within each time step, the task-relevantlatent representationcan be disentangled from the irrelevant part under a simplesparsity regularization, without any additional information or parametric constraints. Together, these results establish a hierarchical foundation: task structure is identifiable across time steps, and task-relevantlatent representations are identifiable within each step. To our knowledge, each result provides a first general nonparametricidentifiabilityguarantee, and together they mark a step toward provably moving from generalist to specialist models.

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