Paper Citation Record · LEDGER

Graph Concept Bottleneck Models

As of 6 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2508.14255.

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

pith.paper-citation-record.v1
2508.14255 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T22:00:57.209272Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T14:10:17.371479Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

verified exact0
verified fuzzy54
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 007b52a6-ec07-4666-a4c4-137bb84dd863 · outbound

This paper cites Deep residual learning for image recognition.

Graph Concept Bottleneck Models Deep residual learning for image recognition

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.651140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation decb061d-59a7-4049-b080-e02c9ae84342 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Graph Concept Bottleneck Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.695328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 49c35f9d-7010-4884-a484-97157a88d9e8 · outbound

This paper cites Mlp-mixer: An all-mlp architecture for vision.

Graph Concept Bottleneck Models Mlp-mixer: An all-mlp architecture for vision

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.655061Z

Source-reported events for the cited work

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Observation 112e16da-5a7e-47d6-a436-4de3cd3ac38c · outbound

This paper cites Attention is all you need.

Graph Concept Bottleneck Models Attention is all you need

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.535736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e605325b-048b-4abf-bb0f-4ffe2266c999 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

Graph Concept Bottleneck Models BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.646963Z

Source-reported events for the cited work

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Observation 65e238b8-28fc-464c-9ecd-080376427a29 · outbound

This paper cites an unresolved cited work.

Graph Concept Bottleneck Models Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-05-18T22:01:52.528491Z

Source-reported events for the cited work

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Observation 885b8824-3dbb-4982-8562-33e288eaa03c · outbound

This paper cites Language models are few-shot learners.

Graph Concept Bottleneck Models Language models are few-shot learners

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.549521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7372a576-e38f-4cdb-867b-3c586e349f35 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Graph Concept Bottleneck Models Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.546332Z

Source-reported events for the cited work

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Observation d0bbe6b5-9cc1-4a75-8c89-36cf45cc6d1a · outbound

This paper cites Kipf and Max Welling.

Graph Concept Bottleneck Models Kipf and Max Welling

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.622683Z

Source-reported events for the cited work

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Observation 7c5bd28f-9e47-4f88-8e7a-49c3625488e4 · outbound

This paper cites Graph contrastive learning with augmentations.

Graph Concept Bottleneck Models Graph contrastive learning with augmentations

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.539028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cc33999c-eeb5-46f8-bdde-2e3c839bc45c · outbound

This paper cites Deep graph learning: Foundations, advances and applications.

Graph Concept Bottleneck Models Deep graph learning: Foundations, advances and applications

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.625883Z

Source-reported events for the cited work

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Observation a1113e1e-a9ca-483c-b276-0eb88c22c5ea · outbound

This paper cites Concept bottleneck models.

Graph Concept Bottleneck Models Concept bottleneck models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.682484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9a78e887-ac9b-4cb4-8581-79c2d6f9d0dd · outbound

This paper cites Neural representations for object perception: structure, category, and adaptive coding.

Graph Concept Bottleneck Models Neural representations for object perception: structure, category, and adaptive coding

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.686015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 05b349b3-be81-40e5-bbcd-66eeb3ac843b · outbound

This paper cites Concept embedding models: Beyond the accuracy-explainability trade-off.

Graph Concept Bottleneck Models Concept embedding models: Beyond the accuracy-explainability trade-off

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.692070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cca395cc-e4f8-4ad9-814e-26075377464c · outbound

This paper cites Probabilistic concept bottleneck models.

Graph Concept Bottleneck Models Probabilistic concept bottleneck models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.705275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3d84b5e3-bed7-4d8e-b996-1f5f13fa6115 · outbound

This paper cites Post-hoc concept bottleneck models.

Graph Concept Bottleneck Models Post-hoc concept bottleneck models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.675789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f584326f-de81-4878-a94f-8a589f9b85c3 · outbound

This paper cites Visual objects in context.

Graph Concept Bottleneck Models Visual objects in context

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.661403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9c1a94de-f729-418e-acd9-e0597c6f31cd · outbound

This paper cites The role of context in object recognition.

Graph Concept Bottleneck Models The role of context in object recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.664879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ebd20a0b-1c5d-4dfb-8059-cb03608b9496 · outbound

This paper cites Addressing leakage in concept bottleneck models.

Graph Concept Bottleneck Models Addressing leakage in concept bottleneck models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.668010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation eff75df3-59c9-4d01-8b93-aaa67d30f0d1 · outbound

This paper cites Energy-based concept bottleneck models: Unifying prediction, concept intervention, and probabilistic interpretations.

Graph Concept Bottleneck Models Energy-based concept bottleneck models: Unifying prediction, concept intervention, and probabilistic interpretations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.643214Z

Source-reported events for the cited work

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Observation 442a83b9-34b8-4669-9b46-35e5f651c92e · outbound

This paper cites Nguyen, and Tsui-Wei Weng.

Graph Concept Bottleneck Models Nguyen, and Tsui-Wei Weng

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.658413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0f80236c-08d6-412e-96e3-37eddf072a8c · outbound

This paper cites Language in a bottle: Language model guided concept bottlenecks for interpretable image classification.

Graph Concept Bottleneck Models Language in a bottle: Language model guided concept bottlenecks for interpretable image classification

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.679527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f072f1ec-ae16-45e1-a081-1b3c307dce54 · outbound

This paper cites Learning concise and descriptive attributes for visual recognition.

Graph Concept Bottleneck Models Learning concise and descriptive attributes for visual recognition

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.672027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b93fe3cf-3488-4f30-a57d-bf3faa1c0698 · outbound

This paper cites Learning graphs from data: A signal representation perspective.

Graph Concept Bottleneck Models Learning graphs from data: A signal representation perspective

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.698722Z

Source-reported events for the cited work

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Observation 03a62869-6fe0-403e-b089-ac0bced1244b · outbound

This paper cites DAG-GNN: DAG structure learning with graph neural networks.

Graph Concept Bottleneck Models DAG-GNN: DAG structure learning with graph neural networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.600943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0f3340e6-0517-4e68-a0f4-c5a91620ba73 · outbound

This paper cites Learning discrete structures for graph neural networks.

Graph Concept Bottleneck Models Learning discrete structures for graph neural networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.594045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bd6335dd-e285-4592-9e84-ac6d3af180d6 · outbound

This paper cites Neural relational inference for interacting systems.

Graph Concept Bottleneck Models Neural relational inference for interacting systems

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.590876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d2082567-a938-443b-bf7c-f7589dac1ff1 · outbound

This paper cites Discrete graph structure learning for forecasting multiple time series.

Graph Concept Bottleneck Models Discrete graph structure learning for forecasting multiple time series

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.629241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d4b36bf9-28d1-4c38-a243-021f73f7cfdb · outbound

This paper cites Differentiable graph module (dgm) for graph convolutional networks.

Graph Concept Bottleneck Models Differentiable graph module (dgm) for graph convolutional networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.581269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0ee4528d-5dbb-455b-b27c-590e81b5ca66 · outbound

This paper cites Federated learning of models pre-trained on different features with consensus graphs.

Graph Concept Bottleneck Models Federated learning of models pre-trained on different features with consensus graphs

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.632633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 79e01fac-7132-42a8-b25a-6a60df92b959 · outbound

This paper cites Augmentations in hypergraph contrastive learning: Fabricated and generative.

Graph Concept Bottleneck Models Augmentations in hypergraph contrastive learning: Fabricated and generative

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.639663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 93cf6940-ef7a-4eba-baf6-3b9901fd2a28 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Graph Concept Bottleneck Models A simple framework for contrastive learning of visual representations

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.518612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6bf8492b-3842-47e7-a35d-d53308f4451a · outbound

This paper cites struc2vec: Learning node representations from structural identity.

Graph Concept Bottleneck Models struc2vec: Learning node representations from structural identity

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.619425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f4e80a60-33d3-48cb-931b-f5bc629d4c3b · outbound

This paper cites Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization.

Graph Concept Bottleneck Models Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.616434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9bb1fee4-299c-4a8a-8b3f-0dd39d1e6a1c · outbound

This paper cites Graph contrastive learning with augmentations.

Graph Concept Bottleneck Models Graph contrastive learning with augmentations

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.613242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 75b0a30e-02d3-44e7-9fa4-c5cff4ca587d · outbound

This paper cites Multi-level contrastive learning framework for sequential recommendation.

Graph Concept Bottleneck Models Multi-level contrastive learning framework for sequential recommendation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.521709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 220bc1f2-e65f-4a49-a8ae-b493819f6c43 · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere.

Graph Concept Bottleneck Models Understanding contrastive representation learning through alignment and uniformity on the hypersphere

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.604205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a996345b-4118-4ca0-834e-d080f766904b · outbound

This paper cites an unresolved cited work.

Graph Concept Bottleneck Models Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-05-18T22:01:52.702096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f235b8c2-ca31-40d5-9be0-61597ca9108a · outbound

This paper cites Automated flower classification over a large number of classes.

Graph Concept Bottleneck Models Automated flower classification over a large number of classes

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.607401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 449981e6-dcdf-46c1-82a6-9b269caefe8f · outbound

This paper cites Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly.

Graph Concept Bottleneck Models Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.610446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bbfdccc0-3645-43b6-9290-d67a5a7430d6 · outbound

This paper cites Learning multiple layers of features from tiny images.

Graph Concept Bottleneck Models Learning multiple layers of features from tiny images

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.553012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8bcce076-d7be-4990-8b41-b0c3c23a68b4 · outbound

This paper cites The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.

Graph Concept Bottleneck Models The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.636137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bf0be85c-2997-475d-a698-8978daafdc7f · outbound

This paper cites Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison.

Graph Concept Bottleneck Models Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.689133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4a91dbfd-ac2d-4297-aaaa-8b572c97d170 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Graph Concept Bottleneck Models Learning transferable visual models from natural language supervision

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.524773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5a7c3c82-a8cb-4a23-8a8a-7563275af947 · outbound

This paper cites Learning to exploit temporal structure for biomedical vision-language processing.

Graph Concept Bottleneck Models Learning to exploit temporal structure for biomedical vision-language processing

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.584446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8a03fb62-1cf0-463e-93b6-62e626c321d7 · outbound

This paper cites Learning bottleneck concepts in image classification.

Graph Concept Bottleneck Models Learning bottleneck concepts in image classification

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.587761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e9de3a9c-c3a7-44cc-869f-50ebaf741f6e · outbound

This paper cites Panousis, Dino Ienco, and Diego Marcos.

Graph Concept Bottleneck Models Panousis, Dino Ienco, and Diego Marcos

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.574402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3eb429f5-a507-4cb4-b5dd-084a6878b42c · outbound

This paper cites Incremental residual concept bottleneck models.

Graph Concept Bottleneck Models Incremental residual concept bottleneck models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.577774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7d5341f3-64d5-488f-a0fe-395f997e2aa7 · outbound

This paper cites Stochastic concept bottleneck models.

Graph Concept Bottleneck Models Stochastic concept bottleneck models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.542626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fe139197-0c91-44b1-971f-5e837a74acbd · outbound

This paper cites A closer look at the intervention procedure of concept bottleneck models.

Graph Concept Bottleneck Models A closer look at the intervention procedure of concept bottleneck models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.571208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3b93179c-d65e-4477-92ad-09ba5f7dbefe · outbound

This paper cites Training language models to follow instructions with human feedback.

Graph Concept Bottleneck Models Training language models to follow instructions with human feedback

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.564983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a146e998-b0f5-457c-886c-68fc15406427 · outbound

This paper cites Linear explanations for individual neurons.

Graph Concept Bottleneck Models Linear explanations for individual neurons

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.531854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 706f5e5d-7161-4f0a-8632-4effc69880f7 · outbound

This paper cites Torchvision: Pytorch’s computer vision library.https://github.

Graph Concept Bottleneck Models Torchvision: Pytorch’s computer vision library.https://github

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.561763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bce3ea66-2c6a-4107-8a0c-5dfe574867f7 · outbound

This paper cites Semantic-aware scene recognition.

Graph Concept Bottleneck Models Semantic-aware scene recognition

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.568062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e2a3fab5-5e69-4b1c-b02d-57a5eaf34add · outbound

This paper cites No Finding.

Graph Concept Bottleneck Models No Finding

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.558982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 50bb5143-b4fa-430f-83ce-8832d7c1cff8 · outbound

This paper cites Pleural Other.

Graph Concept Bottleneck Models Pleural Other

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T22:01:52.597225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6a59761e-3833-4487-96ef-ce653c46650e · outbound

This paper cites an unresolved cited work.

Graph Concept Bottleneck Models Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-05-18T22:01:52.556372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

Observation 925c418c-6fe1-4ced-9ea5-d2690aa64c1d · inbound

A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding cites this paper.

A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding Graph Concept Bottleneck Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T14:10:17.371479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.