Paper Citation Record · LEDGER

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 2 inbound Pith citation observations for arXiv:2506.21102.

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

pith.paper-citation-record.v1
2506.21102 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:42:27.205769Z

measured 35 of 35 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T06:33:59.907537Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T06:37:27.002571Z

Reference resolution

33 of 33 outbound references displayed

verified exact2
verified fuzzy16
unresolved14
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 76de536b-f458-40da-b216-d4c2984408cc · outbound

This paper cites Concept bottleneck models.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Concept bottleneck models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:24.810047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca996c1f-c104-4416-b96c-110a72788d25 · outbound

This paper cites Towards robust interpretability with self-explaining neural networks.Advances in neural information processing systems, 31, 2018.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Towards robust interpretability with self-explaining neural networks.Advances in neural information processing systems, 31, 2018

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:30.444395Z

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.

Observation a0b3c2e3-7b2e-43dc-ac2c-03a1714fb113 · outbound

This paper cites Concept whitening for interpretable image recognition.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Concept whitening for interpretable image recognition

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:24.954548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de0864f4-386b-41a4-b180-c40da415dfc5 · outbound

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

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Concept embedding models: Beyond the accuracy-explainability trade-off

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:30.289472Z

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.

Observation 1f2504b7-f7db-4e49-8bc4-cf4f9144ecc7 · outbound

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

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Promises and Pitfalls of Black-Box Concept Learning Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:25.138243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 596ef094-9e9f-4e13-a1f9-346429a18e53 · outbound

This paper cites Interpretable concept-based memory reasoning.Advances of neural information processing systems 37, NeurIPS 2024, 2024.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Interpretable concept-based memory reasoning.Advances of neural information processing systems 37, NeurIPS 2024, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:30.146438Z

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.

Observation 8d634590-26bd-4bd8-a6c5-0e7e39e58924 · outbound

This paper cites Interpretable neural-symbolic concept reasoning.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Interpretable neural-symbolic concept reasoning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:29.995884Z

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.

Observation b09e6a33-eb76-479e-a877-82309abac685 · outbound

This paper cites Concept- based explainable artificial intelligence: A survey.arXiv preprint arXiv:2312.12936, 2023.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Concept- based explainable artificial intelligence: A survey.arXiv preprint arXiv:2312.12936, 2023

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:25.303853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39dcf176-2486-456c-844f-2c9a6004a297 · outbound

This paper cites Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:25.397684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79aaeeef-11f6-4bcf-938f-469b9fc8bbbe · outbound

This paper cites Stochastic concept bottleneck models.Advances in Neural Information Processing Systems, 37:51787–51810, 2024.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Stochastic concept bottleneck models.Advances in Neural Information Processing Systems, 37:51787–51810, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:29.852972Z

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.

Observation c7dfb0cb-3d0c-48e9-8074-ef61a55f24e0 · outbound

This paper cites Learning to receive help: Intervention-aware concept embedding models.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Learning to receive help: Intervention-aware concept embedding models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:29.679673Z

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.

Observation 62516c2c-1db4-4c64-b79a-1971fc60ce11 · outbound

This paper cites Addressing leakage in concept bottleneck models.Advances in Neural Information Processing Systems, 35:23386–23397, 2022.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Addressing leakage in concept bottleneck models.Advances in Neural Information Processing Systems, 35:23386–23397, 2022

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:29.509916Z

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.

Observation 1626ef52-941c-4f50-b939-f1c98ee38880 · outbound

This paper cites Multilayer feedforward networks are universal approximators.Neural networks, 2(5):359–366, 1989.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Multilayer feedforward networks are universal approximators.Neural networks, 2(5):359–366, 1989

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:25.734082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aee9745d-f7c1-410f-9a33-08249b8e35b6 · outbound

This paper cites Constraint-Free Structure Learning with Smooth Acyclic Orientations.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Constraint-Free Structure Learning with Smooth Acyclic Orientations

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:42:27.569607Z

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.

Observation 545c358f-f5f0-474f-a11f-dadb6b21eb93 · outbound

This paper cites Glancenets: Interpretable, leak-proof concept-based models.Advances in Neural Information Processing Systems, 35:21212–21227, 2022.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Glancenets: Interpretable, leak-proof concept-based models.Advances in Neural Information Processing Systems, 35:21212–21227, 2022

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:29.372557Z

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.

Observation 3da29378-5064-42c1-9d36-a003df6a2d05 · outbound

This paper cites Caltech-ucsd birds 200.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Caltech-ucsd birds 200

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:29.229512Z

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.

Observation 5cca431a-6bf4-4d6e-abae-81c71fdc45ab · outbound

This paper cites DeepProbLog: Neural Probabilistic Logic Programming.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning DeepProbLog: Neural Probabilistic Logic Programming

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:29.022931Z

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.

Observation 18660e32-e2de-47bc-8133-3afa37d48a3f · outbound

This paper cites Lecun, L.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Lecun, L

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:26.197773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 855e0804-20c1-4644-9da8-6f3d7ed178eb · outbound

This paper cites Learning multiple layers of features from tiny images.(2009), 2009.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Learning multiple layers of features from tiny images.(2009), 2009

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:26.293178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c1ef0a9-a01b-4f53-af5c-d840be3b3aab · outbound

This paper cites Nguyen, and Tsui-Wei Weng.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Nguyen, and Tsui-Wei Weng

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:28.879016Z

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.

Observation 3323cd4d-169d-4e7e-8aaa-5a1252fdab0c · outbound

This paper cites Rlogic: Recursive logical rule learning from knowledge graphs.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Rlogic: Recursive logical rule learning from knowledge graphs

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:28.714627Z

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.

Observation 6fc36355-b1b1-4906-89dd-40ec16d0496a · outbound

This paper cites RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:26.484533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7c8aab2-3bd8-4395-867f-0ce354182712 · outbound

This paper cites Synthesizing Datalog Programs Using Numerical Relaxation.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Synthesizing Datalog Programs Using Numerical Relaxation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:42:27.378909Z

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.

Observation cf68407e-eee0-4b2d-bdae-cce2cd6ca453 · outbound

This paper cites Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:26.613819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec1b5074-c23f-44cc-975d-6257da675dd7 · outbound

This paper cites From perception to programs: regularize, overparameterize, and amortize.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning From perception to programs: regularize, overparameterize, and amortize

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:28.531608Z

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.

Observation 5db6be33-250a-4efc-8e54-40ee95439917 · outbound

This paper cites Gradient-based learning applied to document recognition.IEEE, 86(11):2278–2324, 1998.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Gradient-based learning applied to document recognition.IEEE, 86(11):2278–2324, 1998

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:28.369847Z

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.

Observation a3feec45-1122-4f04-b1e8-2ce24ce4ca16 · outbound

This paper cites Deep residual learning for image recognition.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Deep residual learning for image recognition

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:26.814465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f819cf7b-d867-4c6e-aebe-317f9860f3b4 · outbound

This paper cites Principles of categorization.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Principles of categorization

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:28.207824Z

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.

Observation 9355d640-9f3e-465f-8a39-cce9a0f8e6be · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature machine intelligence, 1(5):206–215, 2019.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature machine intelligence, 1(5):206–215, 2019

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:26.924492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6515d294-1f17-45dd-aaf6-f29b2432ab44 · outbound

This paper cites Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:42:28.053241Z

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.

Observation 3d71d781-8e13-4479-9a64-f4e7faa8abea · outbound

This paper cites This looks like that: deep learning for interpretable image recognition.Advances in neural information processing systems, 32, 2019.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning This looks like that: deep learning for interpretable image recognition.Advances in neural information processing systems, 32, 2019

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:27.068403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 416b6933-77c4-4b29-9c1a-4765d05558d3 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library, 2019.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning Pytorch: An imperative style, high-performance deep learning library, 2019

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:27.126189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 719c627c-677e-471b-be4b-a8f59f5ebb8b · outbound

This paper cites whenever the concept ’black wings’ is predicted as True, the concept ’white wings’ is predicted as False and the task ’pigeon’ is predicted as False.

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning whenever the concept ’black wings’ is predicted as True, the concept ’white wings’ is predicted as False and the task ’pigeon’ is predicted as False

Reference 33

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T22:42:27.899588Z

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.

Pith citing papers

Observation 907d2ea7-8b81-4519-8d7c-f0b6a53ce5d9 · inbound

Hyperbolic Concept Bottleneck Models cites this paper.

Hyperbolic Concept Bottleneck Models Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:01:18.822786Z

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.

Observation 440c7913-b71d-4dd7-a0e6-90d1d7aafd90 · inbound

Hyperbolic Concept Bottleneck Models cites this paper.

Hyperbolic Concept Bottleneck Models Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:37:27.006128Z

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.