Pith. sign in

REVIEW 2 major objections 2 minor 45 references

Multimodal Concept Bottleneck Models

T0 review · 2 major / 2 minor · reviewed 2026-06-26 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Multimodal Concept Bottleneck Models add dual layers to CLIP so image and text embeddings align to shared concepts for interpretable zero-shot tasks.

desk verdict MM-CBM extends CBMs to CLIP via dual bottlenecks but needs more method details and leakage checks to back the big claims. read the letter →

arxiv 2606.19882 v1 pith:LT7RIXCQ submitted 2026-06-18 cs.CV cs.LG

Tongqing Shi , Ge Yan , Tuomas Oikarinen , Tsui-Wei Weng This is my paper · ORCID
classification cs.CVcs.LG
keywords conceptbottleneckmodelsmultimodallearningCLIPinterpretabilityzero-shotclassificationimageretrievalvision-language
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper addresses two limits of standard Concept Bottleneck Models: they cannot generalize past a fixed set of classes and they risk using predictive signals outside the intended concepts. MM-CBM places dual Concept Bottleneck Layers inside CLIP to map both image and text embeddings onto the same predefined concepts. This change supports zero-shot classification and image retrieval that remain traceable to the chosen concepts. On four benchmarks the resulting model improves accuracy by as much as 51.26 percent on average while staying within roughly five percent of ordinary black-box performance.

What carries the argument

Dual Concept Bottleneck Layers that project CLIP image and text embeddings into a shared space of predefined concepts.

What would settle it

A test set of zero-shot classification or retrieval examples whose correct answers require concepts absent from the predefined set; if MM-CBM accuracy falls more than five percent below black-box performance or loses interpretability, the alignment claim fails.

Watch

Extended reading notes

Core claim

MM-CBM utilizes dual Concept Bottleneck Layers to align both the image and text embeddings into interpretable features. This allows us to perform new vision tasks like zero-shot classification or image retrieval in an interpretable way. Compared to existing methods, MM-CBM achieves up to 51.26% accuracy improvement on average across four standard benchmarks while maintaining high accuracy, staying within ~5% of black-box performance.

Load-bearing premise

The dual concept bottleneck layers align image and text embeddings to shared concepts without significant information leakage or loss of generalization beyond the training concepts.

Editorial extensions

If this is right

  • Zero-shot classification decisions become traceable to individual concept activations.
  • Image retrieval can rank results by matching concept vectors rather than raw embeddings.
  • Accuracy on standard benchmarks rises by up to 51 percent relative to earlier CBMs.
  • Performance remains within five percent of non-interpretable CLIP baselines.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same dual-layer pattern could be inserted into other vision-language models that produce separate image and text embeddings.
  • If the alignment holds, the set of concepts could be grown after training without retraining the backbone.
  • Direct measurement of concept leakage on held-out tasks would give a quantitative check on the no-leakage premise.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes the Multimodal Concept Bottleneck Model (MM-CBM) that extends standard Concept Bottleneck Models to CLIP by introducing dual Concept Bottleneck Layers (CBLs). These layers map both image and text embeddings onto a shared set of predefined concepts, enabling interpretable zero-shot classification and image retrieval while claiming to mitigate non-concept information leakage. Across four standard benchmarks the method is reported to deliver up to 51.26 % average accuracy improvement relative to prior CBMs and to remain within ~5 % of black-box CLIP performance.

Significance. If the dual-CBL construction demonstrably discards all non-concept signals and the reported gains survive standard controls for concept selection and statistical significance, the work would meaningfully extend the CBM paradigm into the multimodal zero-shot regime. The combination of interpretability with competitive zero-shot retrieval and classification would be a useful addition to the vision-language literature.

major comments (2)
  1. [Abstract] Abstract: the headline claim of 51.26 % average accuracy improvement and the assertion that dual CBLs prevent non-concept leakage are presented without any description of concept vocabulary construction, training procedure, or error bars. Because these details are load-bearing for both the performance and the interpretability claims, the abstract alone does not allow verification that the gains survive ordinary controls.
  2. [Method / Experiments] The central modeling assumption (that the chosen concept set spans essentially all variance in the CLIP embeddings) is not accompanied by any reported measurement of mutual information between post-CBL activations and the original embeddings, nor by an ablation that isolates concept-only versus full-embedding performance on the zero-shot tasks. Without such evidence the leakage concern raised in the skeptic note remains unaddressed.
minor comments (2)
  1. [Method] Notation for the two CBLs and the shared concept basis should be introduced with explicit equations rather than prose only.
  2. [Experiments] The four benchmarks are not named in the abstract; a table listing per-dataset numbers, concept counts, and black-box baselines would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We respond point-by-point to the major comments below, agreeing to revisions that improve clarity and evidence while defending the core methodological choices where they are supported by the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim of 51.26 % average accuracy improvement and the assertion that dual CBLs prevent non-concept leakage are presented without any description of concept vocabulary construction, training procedure, or error bars. Because these details are load-bearing for both the performance and the interpretability claims, the abstract alone does not allow verification that the gains survive ordinary controls.

    Authors: We agree the abstract is too concise for standalone verification. In the revision we will add a brief clause describing the concept vocabulary (derived from standard vision-language datasets aligned via CLIP text encoder) and the dual-CBL training objective. Error bars from repeated runs will be reported for all accuracy figures, and the 51.26 % figure will be explicitly defined as the mean relative improvement over prior CBM baselines across the four benchmarks. revision: yes

  2. Referee: [Method / Experiments] The central modeling assumption (that the chosen concept set spans essentially all variance in the CLIP embeddings) is not accompanied by any reported measurement of mutual information between post-CBL activations and the original embeddings, nor by an ablation that isolates concept-only versus full-embedding performance on the zero-shot tasks. Without such evidence the leakage concern raised in the skeptic note remains unaddressed.

    Authors: The dual-CBL construction projects both image and text embeddings onto an explicit shared concept space, which removes non-concept dimensions by design rather than by post-hoc filtering. While mutual-information statistics were not computed in the submitted version, the observed performance gap of only ~5 % relative to black-box CLIP on zero-shot tasks supplies supporting evidence that task-relevant information is retained. We will add an ablation comparing concept-bottleneck versus full-embedding performance and include mutual-information measurements between post-CBL and original embeddings in the revised experiments section. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; claims are empirical results, not derived quantities

full rationale

The paper reports empirical accuracy improvements (up to 51.26% avg. gain, within ~5% of black-box) from the MM-CBM architecture using dual CBLs on four benchmarks. No equations, derivations, or first-principles predictions appear in the abstract. The central claims rest on experimental outcomes rather than any fitted parameter renamed as a prediction, self-definitional mapping, or load-bearing self-citation chain. The method description (aligning embeddings to concepts) does not reduce to its inputs by construction; performance numbers are measured externally on held-out data. This is the common case of a self-contained empirical contribution.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

The central claim rests on the unstated assumption that a fixed set of human concepts can be aligned to both modalities without loss of task performance and that the chosen benchmarks adequately test generalization and leakage.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Multimodal Concept Bottleneck Models." pith.science (2026). https://pith.science/paper/LT7RIXCQ

@misc{pith2026260619882,
  author       = {Pith},
  title        = {Pith review of: Multimodal Concept Bottleneck Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LT7RIXCQ}},
  note         = {Machine review of arXiv:2606.19882}
}
read the original abstract

Concept Bottleneck Models (CBMs) enhance the interpretability of deep learning networks by aligning the features extracted from images with natural concepts. However, existing CBMs are constrained in their ability to generalize beyond a fixed set of predefined classes and the risk of non-concept information leakage, where predictive signals outside the intended concepts are inadvertently exploited. In this paper, we propose Multimodal Concept Bottleneck Model (MM-CBM) to address these issues and extend CBMs into CLIP. MM-CBM utilizes dual Concept Bottleneck Layers (CBLs) to align both the image and text embeddings into interpretable features. This allows us to perform new vision tasks like zero-shot classification or image retrieval in an interpretable way. Compared to existing methods, MM-CBM achieves up to 51.26% accuracy improvement on average across four standard benchmarks. Our method maintains high accuracy, staying within ~5% of black-box performance while offering greater interpretability.

Figures

Figures reproduced from arXiv: 2606.19882 by the authors.

Figure 1
Figure 1. A. Previous work can compensate for uncertain concept bottlenecks by adjusting the linear layer. B. Our MM-CBM makes predictions based solely on concept responses. C. The inference process of MM-CBM. As an alternative, researchers have developed intrinsically interpretable models such as Concept Bottleneck Models (CBMs) [16, 24, 35, 29, 37], which introduce a human-interpretable concept bottleneck layer (CBL) before… view at source ↗
Figure 2
Figure 2. Overview of MM-CBM. A. Extracting high-quality concept annotations for each modality. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Image Retrieval on five different types of queries. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

45 extracted references · 6 canonical work pages

  1. [1]

    Network dissection: Quantifying interpretability of deep visual representations

    David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba. Network dissection: Quantifying interpretability of deep visual representations. InCVPR, pages 6541–6549, 2017

    2017

  2. [2]

    Interpreting clip with sparse linear concept embeddings (splice)

    Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flavio Calmon, and Himabindu Lakkaraju. Interpreting clip with sparse linear concept embeddings (splice). InAdvances in Neural Information Processing Systems, pages 84298–84328, 2024

    2024

  3. [3]

    Language models can ex- plain neurons in language models

    Steven Bills, Nick Cammarata, Dan Mossing, Henk Tillman, Leo Gao, Gabriel Goh, Ilya Sutskever, Jan Leike, Jeff Wu, and William Saunders. Language models can ex- plain neurons in language models. https://openaipublic.blob.core.windows.net/ neuron-explainer/paper/index.html, 2023

    2023

  4. [4]

    Food-101–mining discriminative components with random forests

    Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101–mining discriminative components with random forests. InComputer vision–ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part VI 13, pages 446–461. Springer, 2014

    2014

  5. [5]

    Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

    1901

  6. [6]

    Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

    Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3558–3568, 2021

    2021

  7. [7]

    Adaptive concept bottleneck for foundation models under distribution shifts.arXiv preprint arXiv:2412.14097, 2024

    Jihye Choi, Jayaram Raghuram, Yixuan Li, and Somesh Jha. Adaptive concept bottleneck for foundation models under distribution shifts.arXiv preprint arXiv:2412.14097, 2024

    work page arXiv 2024

  8. [8]

    Describing textures in the wild

    Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 3606–3613, 2014

    2014

Show all 45 references
  1. [9]

    Imagenet: A large- scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large- scale hierarchical image database. In2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009

    2009

  2. [10]

    Multimodal neurons in artificial neural networks.Distill, 6(3): e30, 2021

    Gabriel Goh, Nick Cammarata, Chelsea V oss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah. Multimodal neurons in artificial neural networks.Distill, 6(3): e30, 2021

    2021

  3. [11]

    Natural language descriptions of deep visual features

    Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, and Jacob Andreas. Natural language descriptions of deep visual features. InICLR, 2021

    2021

  4. [12]

    Distilling the knowledge in a neural network

    Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015

    2015 arXiv

  5. [13]

    Identifying interpretable subspaces in image representations

    Neha Kalibhat, Shweta Bhardwaj, C Bayan Bruss, Hamed Firooz, Maziar Sanjabi, and Soheil Feizi. Identifying interpretable subspaces in image representations. InICML, pages 15623– 15638. PMLR, 2023

    2023

  6. [14]

    Concept-monitor: Understand- ing dnn training through individual neurons.arXiv preprint arXiv:2304.13346, 2023

    Mohammad Ali Khan, Tuomas Oikarinen, and Tsui-Wei Weng. Concept-monitor: Understand- ing dnn training through individual neurons.arXiv preprint arXiv:2304.13346, 2023

    2023

  7. [15]

    Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980, 2014

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980, 2014

    2014 arXiv

  8. [16]

    Concept bottleneck models

    Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. Concept bottleneck models. InInternational conference on machine learning, pages 5338–5348. PMLR, 2020

    2020

  9. [17]

    Learning multiple layers of features from tiny images

    Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 10

    2009

  10. [18]

    Scaling language-image pre-training via masking

    Yanghao Li, Haoqi Fan, Ronghang Hu, Christoph Feichtenhofer, and Kaiming He. Scaling language-image pre-training via masking. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 23390–23400, 2023

    2023

  11. [19]

    Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

    2023

  12. [20]

    Visual classification via description from large language models

    Sachit Menon and Carl V ondrick. Visual classification via description from large language models. InICLR, 2023

    2023

  13. [21]

    Scaling open-vocabulary object detection

    Matthias Minderer, Alexey Gritsenko, and Neil Houlsby. Scaling open-vocabulary object detection. InNeurIPS, 2024

    2024

  14. [22]

    Clip-dissect: Automatic description of neuron rep- resentations in deep vision networks

    Tuomas Oikarinen and Tsui-Wei Weng. Clip-dissect: Automatic description of neuron rep- resentations in deep vision networks. InThe Eleventh International Conference on Learning Representations, 2023

    2023

  15. [23]

    Linear explanations for individual neurons

    Tuomas Oikarinen and Tsui-Wei Weng. Linear explanations for individual neurons. InInterna- tional Conference on Machine Learning, pages 38639–38662. PMLR, 2024

    2024

  16. [24]

    Label-free concept bottleneck models

    Tuomas Oikarinen, Subhro Das, Lam Nguyen, and Lily Weng. Label-free concept bottleneck models. InICLR, 2023

    2023

  17. [25]

    Cats and dogs

    Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar. Cats and dogs. In2012 IEEE conference on computer vision and pattern recognition, pages 3498–3505. IEEE, 2012

    2012

  18. [26]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. InICML, 2021

    2021

  19. [27]

    A multimodal automated interpretability agent

    Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang, Achyuta Rajaram, Evan Hernandez, Jacob Andreas, and Antonio Torralba. A multimodal automated interpretability agent. In Forty-first International Conference on Machine Learning, 2024

    2024

  20. [28]

    Incremental residual concept bottleneck models

    Chenming Shang, Shiji Zhou, Hengyuan Zhang, Xinzhe Ni, Yujiu Yang, and Yuwang Wang. Incremental residual concept bottleneck models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11030–11040, 2024

    2024

  21. [29]

    Vlg-cbm: Training concept bottleneck models with vision-language guidance

    Divyansh Srivastava, Ge Yan, and Tsui-Wei Weng. Vlg-cbm: Training concept bottleneck models with vision-language guidance. InNeurIPS, 2024

    2024

  22. [30]

    Concept bottleneck large language models.ICLR, 2025

    Chung-En Sun, Tuomas Oikarinen, Berk Ustun, and Tsui-Wei Weng. Concept bottleneck large language models.ICLR, 2025

    2025

  23. [31]

    Eva-clip: Improved training techniques for clip at scale.arXiv preprint arXiv:2303.15389, 2023

    Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang, and Yue Cao. Eva-clip: Improved training techniques for clip at scale.arXiv preprint arXiv:2303.15389, 2023

    2023 arXiv

  24. [32]

    Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timo- thée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023

    2023 arXiv

  25. [33]

    The caltech-ucsd birds-200-2011 dataset

    Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011

    2011

  26. [34]

    Transformers: State-of- the-art natural language processing

    Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. Transformers: State-of- the-art natural language processing. InEMNLP, 2020

    2020

  27. [35]

    Learning concise and descriptive attributes for visual recognition

    An Yan, Yu Wang, Yiwu Zhong, Chengyu Dong, Zexue He, Yujie Lu, William Yang Wang, Jingbo Shang, and Julian McAuley. Learning concise and descriptive attributes for visual recognition. InICCV, 2023. 11

    2023

  28. [36]

    Clip-kd: An empirical study of clip model distillation

    Chuanguang Yang, Zhulin An, Libo Huang, Junyu Bi, Xinqiang Yu, Han Yang, Boyu Diao, and Yongjun Xu. Clip-kd: An empirical study of clip model distillation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15952–15962, 2024

    2024

  29. [37]

    Language in a bottle: Language model guided concept bottlenecks for interpretable image classification

    Yue Yang, Artemis Panagopoulou, Shenghao Zhou, Daniel Jin, Chris Callison-Burch, and Mark Yatskar. Language in a bottle: Language model guided concept bottlenecks for interpretable image classification. InCVPR, 2023

    2023

  30. [38]

    Post-hoc concept bottleneck models

    Mert Yuksekgonul, Maggie Wang, and James Zou. Post-hoc concept bottleneck models. InThe Eleventh International Conference on Learning Representations, 2023

    2023

  31. [39]

    Sigmoid loss for language image pre-training

    Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. InProceedings of the IEEE/CVF international conference on computer vision, pages 11975–11986, 2023. 12 A Appendix A.1 Overview The appendix covers: A.2 concept set...

    2023

  32. [40]

    Concept length:Discard concepts exceeding 30 characters to maintain simplicity and interpretability

  33. [41]

    Similarity is measured via cosine similarity in a joint text embedding space, combining features from the CLIP ViT-B/16 text encoder and the all-mpnet-base-v2 sentence encoder

    Similarity to target classes:Remove concepts overly similar to target class names, as they undermine the explanatory role of the CBM. Similarity is measured via cosine similarity in a joint text embedding space, combining features from the CLIP ViT-B/16 text encoder and the al...

  34. [42]

    If I had to describe this image using only one sentence with the words class, it would be:

    Redundancy removal:Eliminate duplicate or near-synonymous concepts to ensure diversity in the bottleneck layer. Using the same embedding space, any concept with cosine similarity above0.9to an already retained concept is removed. This automated generation and filtering process...

  35. [43]

    By removing negative values, we avoid this ambiguity

    Disambiguating negative responses.As discussed in [ 30], it is often unclear whether a negative activation implies the negation of a concept or its complete absence. By removing negative values, we avoid this ambiguity

  36. [44]

    By zeroing out irrelevant (negative) dimensions, we strengthen the contribution of mean- ingful concepts

    Amplifying relevant concept activations.Since similarity computations involve normaliza- tion, weak activations in high-dimensional spaces can lead to dilution of important signals. By zeroing out irrelevant (negative) dimensions, we strengthen the contribution of mean- ingful...

  37. [45]

    barbershop

    Improving inference reliability and efficiency.Without non-negativity, the product of two negative activations (from image and text encodings) may yield a misleadingly high similarity score, falsely indicating semantic alignment. Enforcing non-negativity eliminates this issue ...

Pith tools

Reviewed June 26, 2026 · model on record in the stance chip above.