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arxiv: 2605.22679 · v1 · pith:HN6UFWAAnew · submitted 2026-05-21 · 💻 cs.CV · cs.LG

Conceptualizing Embeddings: Sparse Disentanglement for Vision-Language Models

Piotr Kubaty , Patryk Marsza{\l}ek , {\L}ukasz Struski , Adam Wr\'obel , Jacek Tabor , Marek \'Smieja This is my paper

Pith reviewed 2026-05-22 05:46 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords vision-language modelssparse disentanglementembedding interpretabilitychange of basisCLIPBLIPpost-hoc transformationtop-k sparsity
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The pith

Vision-language embeddings disentangle into interpretable features via a learned invertible rotation and top-k sparsity without expanding dimension.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents CEDAR as a post-training technique that applies an invertible transformation to the embeddings of models such as CLIP and BLIP. A top-k sparsity constraint then forces semantic content into individual coordinates that align with textual concepts or can be decoded into descriptions. This keeps the original embedding size intact and avoids the redundancy introduced by expanding the representation space. A reader would care because it implies that the apparent mixing of concepts in these models may be an artifact of the coordinate system rather than an inherent property of the learned features.

Core claim

CEDAR learns an invertible transformation of pretrained vision-language embeddings together with a top-k sparsity bottleneck. The resulting axis-aligned coordinates concentrate semantic information so that, in CLIP-like models, each coordinate corresponds to a textual concept and, in generative models such as BLIP, can be decoded into natural language. The approach matches the reconstruction-sparsity performance of overcomplete sparse autoencoders while yielding explanations that are more interpretable and better aligned with human judgments, supporting the view that entanglement can be removed by a change of basis.

What carries the argument

CEDAR: an invertible linear transformation learned post-hoc and paired with a top-k sparsity penalty that aligns semantic content to individual embedding axes.

If this is right

  • Individual coordinates become directly mappable to textual concepts in CLIP-style models.
  • Coordinates in generative vision-language models can be decoded into readable natural-language descriptions.
  • Interpretability improves without sacrificing reconstruction fidelity or increasing embedding size.
  • The need for overcomplete feature expansions is removed if a suitable basis change suffices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same rotation-plus-sparsity idea may transfer to other multimodal embedding spaces where overcomplete expansions are currently used.
  • If the axis alignment proves stable across training runs, it could simplify downstream editing or safety interventions on the model.
  • Testing whether the learned basis remains consistent when the underlying vision-language model is fine-tuned would clarify the method's robustness.

Load-bearing premise

A learned invertible transformation plus top-k sparsity can concentrate semantic information into axis-aligned coordinates that are meaningfully interpretable with textual concepts or decodable descriptions.

What would settle it

A controlled test in which the transformed coordinates show no better human alignment or concept recovery than the original entangled embeddings at matched sparsity levels.

Figures

Figures reproduced from arXiv: 2605.22679 by Adam Wr\'obel, Jacek Tabor, {\L}ukasz Struski, Marek \'Smieja, Patryk Marsza{\l}ek, Piotr Kubaty.

Figure 1
Figure 1. Figure 1: Overview of the CEDAR pipeline. We disentangle representations in vision–language [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of CEDAR architecture and training procedure. Given embeddings from a frozen [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of CEDAR and MSAE interpretations. CEDAR associates concept keywords [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Images with highest activation for neuron 347 in the disentangled space, associated with [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Participants consistently favor CEDAR across preference levels, with a significant share [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of the number of concepts generated by each method being selected. Concepts [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: CEDAR achieves ratings close to the dense model without sparsification and substantially [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Semantic quality of reconstructed embeddings measured by CKNNA [ [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Further qualitative results illustrating CEDAR: human-readable concepts derived from the [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Top-activating samples for neuron 12 in the disentangled representation, aligned with the [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Examples eliciting the strongest responses from neuron 611 in the disentangled space, [PITH_FULL_IMAGE:figures/full_fig_p015_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Samples that maximally activate neuron 203 within the disentangled embedding, reflecting [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: User interface screenshots for two example tasks in Study 1. In the screenshot on the left, [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: User interface screenshots for two example tasks in Study 2. In the screenshot on the left, [PITH_FULL_IMAGE:figures/full_fig_p017_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: User interface screenshots for two example tasks in Study 3. Descriptions generated by [PITH_FULL_IMAGE:figures/full_fig_p018_15.png] view at source ↗
read the original abstract

Vision-language models learn powerful multimodal embeddings, yet their internal semantics remain opaque. While sparse autoencoders (SAEs) can extract interpretable features, they rely on expanding the representation dimension, which compromises the original geometry and introduces redundancy. We introduce CEDAR (Conceptual Embedding Disentanglement via Adaptive Rotation), a post-hoc method that reveals the compositional structure of pretrained embeddings without increasing dimensionality. By learning an invertible transformation with a top-$k$ sparsity bottleneck, CEDAR concentrates semantic information into axis-aligned disentangled coordinates. In CLIP-like architecture, individual coordinates can be interpreted with textual concepts, while for generative models such as BLIP, they can be decoded into natural language descriptions. Experiments demonstrate that CEDAR achieves a competitive reconstruction-sparsity trade-off while producing explanations that are more interpretable and better aligned with human perception. Our results suggest that the apparent entanglement in vision-language representations can be resolved through a suitable change of basis, eliminating the need for overcomplete expansions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces CEDAR (Conceptual Embedding Disentanglement via Adaptive Rotation), a post-hoc method that learns an invertible linear transformation on pretrained vision-language embeddings (e.g., from CLIP and BLIP) followed by a top-k sparsity bottleneck. The goal is to rotate the embedding space so that semantic information concentrates into axis-aligned coordinates that can be interpreted via textual concepts or decoded into natural language descriptions, without expanding dimensionality as in sparse autoencoders. The authors claim this yields a competitive reconstruction-sparsity trade-off and explanations better aligned with human perception, suggesting that apparent entanglement in VLM representations can be resolved by a suitable change of basis.

Significance. If the central empirical claims are substantiated, CEDAR would provide a dimension-preserving alternative to overcomplete sparse autoencoders for interpreting multimodal embeddings. This could simplify analysis of compositional semantics in vision-language models and reduce redundancy in feature extraction pipelines.

major comments (2)
  1. [Abstract] Abstract: the claim of a 'competitive reconstruction-sparsity trade-off' and 'more interpretable and better aligned with human perception' is asserted without any reported metrics (e.g., reconstruction MSE, sparsity ratio, human alignment scores), datasets, or baselines, preventing assessment of whether the data supports the superiority statements.
  2. [Method] Method section (description of the adaptive rotation and optimization): because the transformation is linear and invertible while top-k is applied after the change of basis, any gain in interpretability could arise from selecting high-variance directions rather than from semantic factors becoming independent and axis-aligned; no ablation against a fixed non-learned basis (such as PCA + top-k) is described to isolate the contribution of the learned rotation.
minor comments (1)
  1. [Abstract] Abstract: the final sentence states that entanglement 'can be resolved through a suitable change of basis'; this phrasing should be qualified to reflect that the result is empirical rather than a general proof.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point by point below, indicating where we agree that revisions are warranted and outlining the changes we will make.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of a 'competitive reconstruction-sparsity trade-off' and 'more interpretable and better aligned with human perception' is asserted without any reported metrics (e.g., reconstruction MSE, sparsity ratio, human alignment scores), datasets, or baselines, preventing assessment of whether the data supports the superiority statements.

    Authors: We agree that the abstract would be strengthened by including concrete quantitative support for the claims. The Experiments section reports reconstruction MSE, sparsity ratios, human alignment scores, and comparisons against baselines on datasets including MS-COCO and Flickr30k. We will revise the abstract to briefly reference these key results and datasets so that the summary claims are directly tied to the reported evidence. revision: yes

  2. Referee: [Method] Method section (description of the adaptive rotation and optimization): because the transformation is linear and invertible while top-k is applied after the change of basis, any gain in interpretability could arise from selecting high-variance directions rather than from semantic factors becoming independent and axis-aligned; no ablation against a fixed non-learned basis (such as PCA + top-k) is described to isolate the contribution of the learned rotation.

    Authors: This is a fair point. Although the rotation is optimized end-to-end with the top-k bottleneck to concentrate semantics, it remains possible that a variance-driven basis could produce similar effects. To isolate the benefit of the learned adaptive rotation, we will add an ablation comparing CEDAR against PCA followed by top-k selection in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper presents CEDAR as a post-hoc learned invertible linear transformation plus top-k sparsity applied to pretrained vision-language embeddings. No equation or step reduces a claimed prediction or disentanglement result to a fitted quantity defined in terms of itself, nor does any load-bearing premise rest on a self-citation chain, imported uniqueness theorem, or ansatz smuggled from prior work. The central claim that a suitable change of basis resolves apparent entanglement is supported by reported reconstruction-sparsity trade-offs and interpretability experiments that remain independent of the target outputs by construction. This is the normal case of a self-contained empirical method.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on the assumption that semantic structure exists in a rotatable basis and that sparsity promotes human-aligned interpretability, but no specific free parameters or invented entities are detailed in the abstract.

free parameters (1)
  • top-k sparsity level
    Hyperparameter controlling the sparsity bottleneck in the transformation learning process.
axioms (1)
  • domain assumption Pretrained vision-language embeddings contain compositional semantic information that can be isolated via an invertible linear transformation.
    Invoked to justify the change-of-basis approach for disentanglement.

pith-pipeline@v0.9.0 · 5723 in / 1135 out tokens · 44449 ms · 2026-05-22T05:46:51.631796+00:00 · methodology

discussion (0)

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Reference graph

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