Loss-Invariant Projections as Passive Probes of Learned Representations

2026-10-05

AI Summary

This study addresses the challenge that intrinsic structures within neural network representations, independent of task outputs, remain difficult to observe directly. To this end, this work proposes a loss-invariant passive probing paradigm that employs fixed, untrained random projections as passive probes. Combined with S² parameterization analysis and ensemble learning techniques, this approach enables real-time monitoring of geometric evolution and feature accessibility in neural representations without interfering with training. The proposed method reveals distinct evolutionary patterns of information accessibility across regression and classification tasks, effectively disentangling the coupled effects of representational geometry and task difficulty on accessibility. Furthermore, it demonstrates that these passive probes exhibit superior structural independence compared to conventional linear probes.

Abstract

Learned feature representations in neural networks often contain structure beyond that directly used by the final task output. We study this structure using $\textit{passive probes}$ that apply fixed, untrained, property-independent projections to representations as they evolve during training. We motivate this approach through the task of prediction on $S^2$ where equivalent vector and Hermitian parameterizations reveal an additional loss-invariant trace coordinate. This motivates a general construction in which fixed random projections serve as observers of learned features. Because the observer is loss-invariant and independent of the property being studied, changes in accessibility reflect changes in the representation relative to the fixed observer rather than adaptation of the observer itself. We show that ensembles of passive probes can directly reflect task-relevant information such as target alignment. Under our constructions, the accessibility of eventual difficulty evolves differently across tasks. It increases during training in the regression tasks of surface-normal estimation and image inpainting but remains near its initial level in image classification. Comparisons with learned linear probes further show that recoverability and passive accessibility can evolve differently during training. Together, these results show how passive probes can separately characterize changes in representation geometry and the accessibility of eventual task difficulty.

Problem

• learned representations
• passive probes
• loss-invariant projections
• representation geometry
• task difficulty

Innovation

• Passive Probes
• Loss-Invariant Projections
• Learned Representations
• Representation Geometry
• Random Projections