Do latent variables learned by deep models represent real-world sources or artifacts?

Determine whether hidden latent variables learned by deep machine learning models contain information about the real-world source generating the data or whether the internal-state representations are artifacts of the training and model architecture.

Background

The review highlights opacity challenges specific to ML models in scientific contexts, notably whether learned latent representations correspond to genuine features of the physical systems under study or are merely artifacts of model training.

Resolving this uncertainty is critical for trusting ML-derived scientific insights; if latent variables do reflect real sources, they can serve as interpretable, physically meaningful descriptors. If they are artifacts, reliance on them may mislead scientific inference.

References

It is often unclear whether the hidden latent variables within the model (see Sec.~\ref{chapter:algorithms}) contain information about the source of the data or if the representations of the internal states are merely artifacts .

— Interpretable Machine Learning in Physics: A Review  (2503.23616 - Wetzel et al., 30 Mar 2025) in Section 3.2, The opacity/black-box problem

The observed coordinates are best read as geometry of source proposals shaped by changed files and lineages; the experiment did not test whether they correspond to distinct mechanisms or runtime behavior.

— Loreley: Repository-Scale Program Evolution with Quality-Diversity Search  (2608.19703 - Chen, 20 Aug 2026) in Section 5.2, “Archive and descriptor behavior”

The source of this structure remains unresolved. Words can acquire associations from surrounding visual content or through text embedded in frames; the latter could provide linguistic co-occurrence signals even for semantic relations not directly instantiated in the depicted scene. Embedded text influences CLIP training (Lin et al., 2024); it is also a potential source of semantic information in DINOv3’s images, which are drawn mainly from public Instagram posts (Siméoni et al., 2026). Image-only training therefore need not mean learning exclusively from depicted, concrete objects; vision models’ knowledge of abstract semantics remains an open question.

— Vision Transformers Learn Word Meaning from Images Alone  (2610.13252 - Gjølbye et al., 9 Oct 2026) in Section 7, “The scope and possible sources of semantic knowledge”

In the context of AD, probing the structure of learned representation spaces remains an open direction.

— Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC  (2610.13437 - Genovese et al., 9 Oct 2026) in Section 2.3, Representation Analysis and Model Diagnostics in High-Energy Physics

When Sparse Autoencoder (SAE) \citep{bricken2023monosemanticity} decomposes a model's residual stream into a sparse sum of monosemantic features, we cannot know whether those features correspond to genuine computational units or are artifacts of the decomposition method \citep{korznikov2026sanity, leask2025sparse}.

— Sparse Transformer: a Controlled Testbed for Mechanistic Interpretability  (2610.19907 - Korznikov et al., 14 Oct 2026) in Section 1, Introduction