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arxiv: 2606.21197 · v1 · pith:NEVAX5SDnew · submitted 2026-06-19 · 💻 cs.CV · cs.AI· cs.LG

Extraction and Analysis of Multimodal Concepts in Vision Language Models through Sparse Autoencoders

Sergio Lanza , Jae Hee Lee , Stefan Wermter This is my paper

Pith reviewed 2026-06-26 14:20 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords sparse autoencodersvision language modelsmultimodal conceptsconcept extractionmodel interpretabilityVQALLaVA
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The pith

A sparse autoencoder framework extracts visual, textual, and multimodal concepts from vision-language models while raising visual concept quality by up to 45 percent.

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

The paper sets out to extract and categorize concepts encoded inside vision-language models by applying sparse autoencoders to both image and text activations. Prior SAE work examined only one modality at a time, so multimodal concepts that draw on both were either missed or mislabeled, and visual descriptions were often vague. The new method generates a candidate human-interpretable label for each neuron and scores its alignment to dataset examples with cosine similarity, producing an explicit classification into visual, textual, or multimodal. On the LLaVA-NeXT VQA dataset the approach yields markedly clearer visual concepts without degrading textual ones and surfaces multimodal concepts in a repeatable way.

Core claim

For each SAE neuron the framework proposes a candidate human-interpretable concept and computes cosine-similarity alignment to samples in a VQA dataset; the resulting scores classify the neuron as encoding a visual, textual, or multimodal concept. Experiments on LLaVA-NeXT show this classification improves visual concept quality by up to 45 percent relative to earlier SAE baselines while preserving high textual quality and systematically revealing multimodal concepts.

What carries the argument

Neuron-wise candidate concept proposal followed by cosine-similarity alignment to dataset samples, used to assign each neuron to visual, textual, or multimodal category.

If this is right

  • Multimodal concepts that integrate image and text can now be isolated rather than misclassified as purely visual or textual.
  • Visual concept descriptions become concrete enough to support detailed tracing of model reasoning in image-text tasks.
  • The same neuron-level classification procedure can be applied to other VLMs without changing the core alignment step.
  • Systematic separation of concept types supplies a clearer map of how VLMs combine modalities inside their representations.

Where Pith is reading between the lines

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

  • The method could be extended to measure how often multimodal neurons participate in correct versus incorrect VQA answers.
  • If the alignment scores prove stable across datasets, they might serve as a lightweight probe for concept drift when models are fine-tuned.
  • Applying the framework to larger or more diverse VLMs would test whether the reported quality gain generalizes beyond LLaVA-NeXT.

Load-bearing premise

The human-proposed candidate labels plus their cosine-similarity scores to data samples correctly reflect the concepts actually encoded by the neurons.

What would settle it

Running the same pipeline on a different VLM or VQA dataset and finding that visual concept quality does not rise or that multimodal neurons cannot be distinguished from unimodal ones would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2606.21197 by Jae Hee Lee, Sergio Lanza, Stefan Wermter.

Figure 1
Figure 1. Figure 1: Overview of our multimodal concept extraction framework. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of the CLIP and ALIGN scores for the proposed model [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative example for intervention experiments using the prompt “ [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Examples of multimodal concepts. The textual tokens that trigger the [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Examples of visual concepts [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Examples of textual concepts.The textual tokens that trigger the SAE [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Labelling Prompt (LLaVA 72B) The last two prompts are used to query Llama 3.1 70B, which evaluates textual concepts (Figures 10 and 11) based on [17] [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visual Generation Guidelines (LLaVA 72B) [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: A4: Textual Generation Guidelines (LLaVA 72B) [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Detection score prompt (Llama 3.1 70B) [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Fuzzing score prompt (Llama 3.1 70B) [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
read the original abstract

Vision Language Models (VLMs) have demonstrated impressive performance in tasks requiring joint understanding of images and text, such as image captioning and Visual Question Answering (VQA), but our understanding of their internal processes remains limited. Recently, Sparse Autoencoders (SAEs) have emerged as a promising tool to support the interpretation of concepts encoded in VLMs. However, most SAE-based approaches focus only on textual or visual concepts separately, ignoring multimodal concepts. This limitation hinders a comprehensive understanding of VLMs, since concepts that integrate both modalities can be misclassified. Moreover, previous visual approaches often produce low-quality visual concept descriptions that are vague or incomplete, limiting their usefulness for understanding model reasoning. We propose a framework based on SAEs to extract and analyze visual, textual, and multimodal concepts from VLMs. For each neuron, we propose a candidate human-interpretable concept and compute the alignment between the concept and the dataset samples using cosine similarity scores. Experiments on a VQA dataset (LLaVA-NeXT) demonstrate that our framework improves visual concept quality by up to 45\% compared to existing SAE-based methods, while maintaining high textual concept quality and enabling systematic identification of multimodal concepts. This work contributes new insights into the conceptual space of VLMs, providing a structured approach to distinguish between visual, textual, and multimodal concepts. The code is available at https://github.com/PHDLanza/Multidata_SAE

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 proposes an SAE-based framework for extracting visual, textual, and multimodal concepts from VLMs. For each neuron, a human-interpretable concept is proposed and aligned to dataset samples via cosine similarity to assign modality categories. On the LLaVA-NeXT VQA dataset, the method is claimed to improve visual concept quality by up to 45% over prior SAE approaches while preserving textual quality and enabling systematic multimodal identification. Code is released at the provided GitHub link.

Significance. If the quality metric and modality assignments prove robust, the work would offer a practical method for distinguishing multimodal concepts in VLMs, addressing a gap in current SAE interpretability literature. The public code release is a clear strength that supports reproducibility and follow-up experiments.

major comments (2)
  1. [Experiments (and abstract)] The 45% visual-quality improvement and the multimodal counts both depend on the neuron-to-modality assignment procedure (human-proposed concept + cosine similarity to samples). No ground-truth modality labels, ablation on the similarity threshold, or downstream-task correlation is reported to confirm that the assigned category matches the neuron’s actual encoding behavior. This is load-bearing for the central quantitative claim.
  2. [Abstract and Experiments] The abstract states a quantitative improvement of 45% but supplies no definition of the visual-concept-quality metric, the exact baseline SAE methods, error bars, number of runs, or statistical test. Without these, the reported gain cannot be evaluated.
minor comments (1)
  1. [Methods] Notation for cosine similarity and the exact SAE reconstruction loss should be stated explicitly in the methods section rather than left implicit.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on experimental validation and clarity. We address each point below and have revised the manuscript accordingly where feasible.

read point-by-point responses

  1. Referee: [Experiments (and abstract)] The 45% visual-quality improvement and the multimodal counts both depend on the neuron-to-modality assignment procedure (human-proposed concept + cosine similarity to samples). No ground-truth modality labels, ablation on the similarity threshold, or downstream-task correlation is reported to confirm that the assigned category matches the neuron’s actual encoding behavior. This is load-bearing for the central quantitative claim.

    Authors: We acknowledge that the modality assignment procedure is central to the claims and that ground-truth labels are unavailable in the LLaVA-NeXT dataset. The cosine-similarity alignment to human-proposed concepts follows standard practice in SAE interpretability when direct labels do not exist. In the revised manuscript we have added an ablation varying the similarity threshold (0.4–0.9) and show that the reported visual-quality gains remain stable. We have also expanded the discussion of assignment limitations and note downstream-task validation as future work; a full correlation study exceeds the scope of the current submission. revision: partial

  2. Referee: [Abstract and Experiments] The abstract states a quantitative improvement of 45% but supplies no definition of the visual-concept-quality metric, the exact baseline SAE methods, error bars, number of runs, or statistical test. Without these, the reported gain cannot be evaluated.

    Authors: We agree the abstract and experimental reporting lacked necessary detail. The revised abstract and Section 4 now define visual-concept-quality as the mean human interpretability rating (1–5 scale) assigned by three annotators to the top-10 activating samples per neuron. Baselines are the SAE variants from the cited prior works on VLMs. Results are reported as means over three independent runs with standard-deviation error bars; a paired t-test (p < 0.05) is included to support the 45 % gain. These clarifications have been incorporated throughout the manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity; method applies external cosine similarity to dataset samples for categorization.

full rationale

The paper describes a framework that proposes human-interpretable concepts per neuron and computes cosine similarity alignments to VQA dataset samples to assign visual/textual/multimodal labels. The reported 45% visual quality improvement is presented as an empirical comparison against prior SAE methods using the same external alignment process. No equations, derivations, or self-citations reduce the central claims to definitional equivalence or fitted inputs by construction. The approach is self-contained against the described external benchmarks and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract alone supplies no concrete information on free parameters, background axioms, or new postulated entities; all such elements remain unknown.

pith-pipeline@v0.9.1-grok · 5799 in / 1132 out tokens · 19094 ms · 2026-06-26T14:20:27.189881+00:00 · methodology

discussion (0)

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