Finite-sample uniform generalization for generative and vision–language models

Determine finite-sample structural conditions under which modern generative models and vision–language models produce predictions that generalize uniformly across inputs, classes, and subpopulations, rather than only on average, so that worst-case errors and miscalibration are controlled across the entire input domain.

Background

The paper focuses on reliability requirements in biomedical applications, where models must be accurate and well calibrated not only on average but uniformly across inputs and subgroups. The authors emphasize that despite strong empirical performance of generative and vision–LLMs with moderate data, there is a key unresolved issue about when such uniform generalization and calibration can be expected in finite-sample regimes.

They propose analyzing induced families of classifiers via prompt embeddings and derive uniform convergence bounds under Lipschitz stability and low effective dimension, but the broader question of identifying general finite-sample conditions for uniform generalization and calibration across diverse settings remains unresolved and motivates their study.

References

While such models often achieve strong empirical performance with moderate data, it remains unclear when their predictions can be expected to generalize uniformly across inputs, classes, or subpopulations, rather than only on average.

— How Much Data Is Enough? Uniform Convergence Bounds for Generative & Vision-Language Models under Low-Dimensional Structure  (2512.23109 - Thompson, 28 Dec 2025) in Abstract

This paper establishes interface reuse across recognition and question answering and measures early transfer; the broader competence and reliability requirements remain open.

— From Text Decisions to Pixels: An Study of Jev-Style Visual Choice Model  (2609.29283 - Zhou et al., 24 Sep 2026) in Section 1, Introduction

Importantly, the guarantees are marginal over the entire population, and thus do not necessarily hold for all policy-relevant subgroups. If calibration data for these groups exists, this could likely be remedied by combining CT and SAFE with class-conditional conformal prediction, as described by \citet{angelopoulos2023conformal}, but we leave this as future work.

— Beyond Point Predictions: Uncertainty-Aware Satellite Poverty Mapping for Public Policy  (2608.23322 - Pettersson et al., 24 Aug 2026) in Discussion, paragraph beginning “The guarantees provided by conformal prediction depend on the calibration data being exchangeable”

This mismatch can distort evaluation by favouring models that fit simplified benchmark distributions while leaving their behaviour in ring-diverse chemical space unresolved.

— Higher-Order Molecular Grammars for Generative and Foundation Models in Chemistry  (2610.02186 - Huang et al., 1 Oct 2026) in Section 3.3, “RingDiv: a ring-diversity benchmark”