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Paper Citation Record · LEDGER

A Comprehensive Survey on the Risks and Limitations of Concept-based Models

As of 9 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2506.04237.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.04237 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:24:34.680815Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

  • verified exact5
  • verified fuzzy2
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 39836d36-c93b-413d-b3cb-345410f5778a · outbound

This paper cites Probabilistic Concept Bottleneck Models.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Probabilistic Concept Bottleneck Models

Reference 5

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source=pdf_text observed=2026-08-07T14:24:33.381480Z digest=sha256:d71d527e8eed586d001b4fe9acd9e8d401b8302a7622d7575e6e6bd9fe95d531

Observation be73b651-e7cf-46f6-9b51-7b4068ce9c6b · outbound

This paper cites CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models

Reference 7

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source=pdf_text observed=2026-08-07T14:24:33.508390Z digest=sha256:2aa106050ed978ef5510f779dd36b8f0687acff96b07972b9c5a3c6a2021c8d9

Observation e91665d0-f2c1-4215-a5c1-67e7f321a60f · outbound

This paper cites Factor Graph-based Interpretable Neural Networks.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Factor Graph-based Interpretable Neural Networks

Reference 8

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local_arxiv, observed 2026-08-07T14:24:35.681270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e6573d2b-dbbb-4196-998e-e30ae835cfba · outbound

This paper cites Promises and Pitfalls of Black-Box Concept Learning Models.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Promises and Pitfalls of Black-Box Concept Learning Models

Reference 9

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source=pdf_text observed=2026-08-07T14:24:33.677368Z digest=sha256:9a987d43eb096634a1db383f5b94745090b705ec66ec3c4adb7b056cc0ed5b83

Observation 9b0de2bb-2e76-4142-ab20-d367b4c84fd0 · outbound

This paper cites Do Concept Bottleneck Models Learn as Intended?.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Do Concept Bottleneck Models Learn as Intended?

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:24:33.724693Z digest=sha256:a6012dde3c35de9fbf0fbb57916bd4e629e56e42cec7aa3b73009762bddc9485

Observation 8111a9b9-f609-4613-aeaf-c86dc14dec5d · outbound

This paper cites Coarse-to-Fine Concept Bottleneck Models.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Coarse-to-Fine Concept Bottleneck Models

Reference 11

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no resolver link, observed 2026-08-07T14:24:33.779857Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:24:33.779857Z digest=sha256:2528f8d453ba4f65f3c2e8b35fbbafa3907ba75b484c11a35ff9e07c19e3575c

Observation ba50047c-5daa-442f-828a-affba3e839c8 · outbound

This paper cites PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck

Reference 12

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verified exact
local_arxiv, observed 2026-08-07T14:24:35.419383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:24:33.841052Z digest=sha256:af3d5c762ab959e427a246ba55483535e554a7ed59bf4a84a8da7bbad6103a2a

Observation b4d79d55-d8ce-49dc-b713-334576626970 · outbound

This paper cites Concept-based explain- able artificial intelligence: A survey.arXiv preprint arXiv:2312.12936,.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Concept-based explain- able artificial intelligence: A survey.arXiv preprint arXiv:2312.12936,

Reference 13

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source=pdf_text observed=2026-08-07T14:24:33.903027Z digest=sha256:55496a0a85123e41e5fa64b488ef99ea6de398b9f20c88af31a58b94286dd042

Observation b87e16c9-ba70-4874-819f-eaac1a3387f1 · outbound

This paper cites Tree-Based Leakage Inspection and Control in Concept Bottleneck Models.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Tree-Based Leakage Inspection and Control in Concept Bottleneck Models

Reference 14

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source=pdf_text observed=2026-08-07T14:24:33.998696Z digest=sha256:71e7980cb131664075e3190c264ce95b5fa2163ee5f0790c3e1c33b60aa28b61

Observation b7043136-e7e4-4e10-9438-bb7a42fa4795 · outbound

This paper cites Do Concept Bottleneck Models Respect Localities?.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Do Concept Bottleneck Models Respect Localities?

Reference 15

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source=pdf_text observed=2026-08-07T14:24:34.046096Z digest=sha256:5ef5d0b6dae91dff5a7a7b4279db52b9e0d14c45aaa1f482a07802d5e4869488

Observation 4937398c-fe6c-4613-b96e-269ed4eb0cc3 · outbound

This paper cites Understanding Inter-Concept Relationships in Concept-Based Models.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Understanding Inter-Concept Relationships in Concept-Based Models

Reference 16

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local_arxiv, observed 2026-08-07T14:24:35.113143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:24:34.088253Z digest=sha256:9e283e77a094707e4e4fc848341a346b0c0cc529794c73f24df19b2e8a071253

Observation e334b584-82b4-4473-a1b0-890503dcd62c · outbound

This paper cites Model-Agnostic Interpretability of Machine Learning.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Model-Agnostic Interpretability of Machine Learning

Reference 17

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source=pdf_text observed=2026-08-07T14:24:34.141079Z digest=sha256:3c2a95defeccbe660f266b88dd4346a729d18480035d9edb40c848cda3b422a8

Observation 15ca1ba9-a213-4b0c-b492-c83d8df814b7 · outbound

This paper cites C-SENN: Contrastive Self-Explaining Neural Network.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models C-SENN: Contrastive Self-Explaining Neural Network

Reference 18

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source=pdf_text observed=2026-08-07T14:24:34.211366Z digest=sha256:2c30be7de6dd9285f2d3a661c414d7e26fcd9f91ca1d845d79f0ee2b582b44e8

Observation b295dee7-1b37-4516-8e7f-c78ae58586f2 · outbound

This paper cites Learn- ing from uncertain concepts via test time interventions.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Learn- ing from uncertain concepts via test time interventions

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:24:34.287799Z digest=sha256:7ba6940285c97efe6c00f7b617599340cdce4ddee80431fcc7bcb86750a0eb99

Observation c8f341a4-9a53-463e-9e7e-3d056e035156 · outbound

This paper cites Learning to Intervene on Concept Bottlenecks.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Learning to Intervene on Concept Bottlenecks

Reference 20

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source=pdf_text observed=2026-08-07T14:24:34.347399Z digest=sha256:62d3f32070b098cadb18427a943b34463ffca85e67f7ca7635a481af6294dc05

Observation f6c050c5-fdd2-4835-919a-38439f81e4af · outbound

This paper cites Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning

Reference 21

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source=pdf_text observed=2026-08-07T14:24:34.401862Z digest=sha256:518b9be3de1c944841cae2ca3e140bb567f4b59e6443104d902bcc045de611a2

Observation 9c101c3e-d1d1-48d7-bec5-803fcb1c6f8a · outbound

This paper cites Toward faithful explanatory active learning with self-explainable neural nets.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Toward faithful explanatory active learning with self-explainable neural nets

Reference 23

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raw_fallback, observed 2026-08-07T14:24:36.221357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:24:34.517172Z digest=sha256:4ce4a5ab918a99fa4dea4a7aa4d6bad3c9eaf86ab3d7762e0a2a9b9c750722e2

Observation 3e1f0b59-9b53-45c0-ba2a-2df0f4e4872a · outbound

This paper cites Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations

Reference 24

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source=pdf_text observed=2026-08-07T14:24:34.609785Z digest=sha256:2a49607916344c38ac5eba3e0065b3ff7e4283a725096d78948f39fe9076a04b

Observation f72111c2-fea4-4cc8-bd27-b3a4a79c95df · outbound

This paper cites Benchmarking and Enhancing Disentanglement in Concept-Residual Models.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Benchmarking and Enhancing Disentanglement in Concept-Residual Models

Reference 25

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local_arxiv, observed 2026-08-07T14:24:34.864623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:24:34.648230Z digest=sha256:849010f96658b0becf90fd8dbf0ea1a0e37be8b695c8cda45f3b87ab2a48ed1d

Observation 3e138ac2-0178-4a23-a877-079f5010fcd4 · outbound

This paper cites Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off

Reference 26

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source=pdf_text observed=2026-08-07T14:24:34.680815Z digest=sha256:7835e1990a46e3e4d013950abc4cf0b6746d46a39e09852d025098e9edefa56c

Observation 29198a8f-c2cf-4c60-a385-e114dd8fa98b · outbound

This paper cites Intriguing properties of neural networks.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Intriguing properties of neural networks

Reference 2017

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source=pdf_text observed=2026-08-07T14:24:34.460084Z digest=sha256:a0ad6fd86904efa4d969fd8ede2912be7b1c7b9b26a63cd35dacdd7b7bbe8a0c

Observation d29af6bc-71e5-4f3c-be8a-bca0ca9a8e42 · outbound

This paper cites Debiasing Concept-based Explanations with Causal Analysis.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Debiasing Concept-based Explanations with Causal Analysis

Reference 2018

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local_arxiv, observed 2026-08-07T14:24:36.044570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:24:33.142790Z digest=sha256:8b55f1875b4de5300b2175555cd93ff73f9a4b17f54cfce645e51cbb6f80a884

Observation 6712675f-d1b1-4f20-a6f5-3f9cac9dc804 · outbound

This paper cites Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models

Reference 2020

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source=pdf_text observed=2026-08-07T14:24:33.427881Z digest=sha256:c205d7a9c9b56d7055754e3e8a27c44127ebeec7755b11e830dc00af39ad1c7f

Observation 22285d18-aa6b-4283-ba48-3e863ede78a4 · outbound

This paper cites Editable Concept Bottleneck Models.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Editable Concept Bottleneck Models

Reference 2022

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source=pdf_text observed=2026-08-07T14:24:33.319146Z digest=sha256:7e5abcb2e7e084edf17d6899ac613f4fd653973bf975ed6bdf16310d04a79683

Observation 41fd212a-d739-44c9-862a-a3ba7d405bd9 · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 2023

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source=pdf_text observed=2026-08-07T14:24:33.231136Z digest=sha256:58b7ca4ce8c13451c88e7eb61a0134b167d6b1efac97975d769e6e7a33800306

Observation 30a84855-750c-456e-84bb-fc64a937964a · outbound

This paper cites Towards Robust Interpretability with Self-Explaining Neural Networks.

A Comprehensive Survey on the Risks and Limitations of Concept-based Models Towards Robust Interpretability with Self-Explaining Neural Networks

Reference 2024

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:24:33.060892Z digest=sha256:0510f4a3d67abaaf024b66c17b5c243f1805a2d060cd126085a25a13b2d12d76

Pith citing papers

No inbound Pith citation observations are available.