Paper Citation Record · LEDGER

Promises and Pitfalls of Black-Box Concept Learning Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2106.13314.

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

pith.paper-citation-record.v1
2106.13314 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:21:13.507624Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

20
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 959d1b59-4b21-4332-a6c2-48abd5d0615f · inbound

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model cites this paper.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Promises and Pitfalls of Black-Box Concept Learning Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T16:21:13.507624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd81d536-d05b-4785-b314-89e0932cb382 · inbound

Survival Concept-Based Learning Models cites this paper.

Survival Concept-Based Learning Models Promises and Pitfalls of Black-Box Concept Learning Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T17:19:58.507640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 722369bb-8e35-455e-a6da-3dec3f4491bf · inbound

Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification cites this paper.

Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification Promises and Pitfalls of Black-Box Concept Learning Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:34.599256Z

Source-reported events for the cited work

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

A Comprehensive Survey on the Risks and Limitations of Concept-based Models cites this paper.

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

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:24:33.677368Z

Source-reported events for the cited work

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

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning cites this paper.

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 68a6465a-8dcf-4b7d-ac69-92aeded309ec · inbound

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs cites this paper.

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs Promises and Pitfalls of Black-Box Concept Learning Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:35.085262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c245e3bb-3d07-4d3f-bd2e-2951dca8bf04 · inbound

Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings cites this paper.

Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings Promises and Pitfalls of Black-Box Concept Learning Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T18:45:20.560966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31d9d04e-2817-4b3e-b37d-fab4c5bd9833 · inbound

The SuperActivator Mechanism: Transformers Concentrate Reliable Concept Signals in the Tail cites this paper.

The SuperActivator Mechanism: Transformers Concentrate Reliable Concept Signals in the Tail Promises and Pitfalls of Black-Box Concept Learning Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T18:33:04.976353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b520773b-03ec-4104-ac92-5244d44bdb55 · inbound

Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures cites this paper.

Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures Promises and Pitfalls of Black-Box Concept Learning Models

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T08:27:51.532641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ab9136d5-fc8d-4787-ad29-f24fbb427d32 · inbound

Prototype-Grounded Concept Models for Verifiable Concept Alignment cites this paper.

Prototype-Grounded Concept Models for Verifiable Concept Alignment Promises and Pitfalls of Black-Box Concept Learning Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:22:37.454641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ea99be16-d30c-4ccf-b73e-5c8af2008ae8 · inbound

Prototype-Grounded Concept Models for Verifiable Concept Alignment cites this paper.

Prototype-Grounded Concept Models for Verifiable Concept Alignment Promises and Pitfalls of Black-Box Concept Learning Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-22T10:11:23.497048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bfcad548-2e19-419f-b719-006263d0da76 · inbound

Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset cites this paper.

Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset Promises and Pitfalls of Black-Box Concept Learning Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:22:20.198451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8c3fc5fe-cab3-4537-bba1-9adda6b475a5 · inbound

Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition cites this paper.

Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition Promises and Pitfalls of Black-Box Concept Learning Models

Reference 73

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:45:58.783166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fde040ac-1b09-4d28-b2f9-efabb28a7e3d · inbound

ShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding cites this paper.

ShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding Promises and Pitfalls of Black-Box Concept Learning Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:37:19.127254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 33883d4e-4413-41dc-b283-b24eb07b4c05 · inbound

Interpretable Discriminative Text Representations via Agreement and Label Disentanglement cites this paper.

Interpretable Discriminative Text Representations via Agreement and Label Disentanglement Promises and Pitfalls of Black-Box Concept Learning Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:49:41.178358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d9844233-6b25-4039-8261-29cfad58c84c · inbound

SynCB: A Synergy Concept-Based Model with Dynamic Routing Between Concepts and Complementary Neural Branches cites this paper.

SynCB: A Synergy Concept-Based Model with Dynamic Routing Between Concepts and Complementary Neural Branches Promises and Pitfalls of Black-Box Concept Learning Models

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T05:49:40.993164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7ef03645-5219-418d-9825-25afba86d504 · inbound

CB-SLICE: Concept-Based Interpretable Error Slice Discovery cites this paper.

CB-SLICE: Concept-Based Interpretable Error Slice Discovery Promises and Pitfalls of Black-Box Concept Learning Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:33:15.860345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bcfa27bf-44c4-4ca7-bc7c-294524553fff · inbound

Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models cites this paper.

Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models Promises and Pitfalls of Black-Box Concept Learning Models

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:16:43.815993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c47f997e-3b37-4eae-b500-f21f6f2464b5 · inbound

Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention cites this paper.

Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention Promises and Pitfalls of Black-Box Concept Learning Models

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T07:06:44.253014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8c0e68a-2655-40cd-954b-d6ae9e906aff · inbound

Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention cites this paper.

Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention Promises and Pitfalls of Black-Box Concept Learning Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T12:29:44.177803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8dec802-fb56-4b52-b2cc-9e1a4f92f8b7 · inbound

In Defense of Information Leakage in Concept-based Models cites this paper.

In Defense of Information Leakage in Concept-based Models Promises and Pitfalls of Black-Box Concept Learning Models

Reference 185

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T04:37:37.639600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3a22dfab-61dd-4242-9fb6-c62cdae647d7 · inbound

Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks cites this paper.

Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks Promises and Pitfalls of Black-Box Concept Learning Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:19:13.795086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9a5eea8f-0b90-4a0d-b933-888fbe3bd349 · inbound

On the Faithfulness of Post-Hoc Concept Bottleneck Models cites this paper.

On the Faithfulness of Post-Hoc Concept Bottleneck Models Promises and Pitfalls of Black-Box Concept Learning Models

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T06:44:19.515508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f4e42de6-bd6c-4b7f-87b1-b86141ece829 · inbound

Caption Bottleneck Models cites this paper.

Caption Bottleneck Models Promises and Pitfalls of Black-Box Concept Learning Models

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T15:07:04.092790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 82c09e5b-7390-4f12-9867-25e5312f2a97 · inbound

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI cites this paper.

ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI Promises and Pitfalls of Black-Box Concept Learning Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-13T01:29:02.336552Z

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Observation 37ade2a8-e326-4dde-b13e-80466244493c · inbound

Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations cites this paper.

Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations Promises and Pitfalls of Black-Box Concept Learning Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T10:03:01.989420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3094537a-9b59-49d8-9cd5-559d2f84fa80 · inbound

Loss Invariance Determines What Concept Layers Encode: Volume Grounding in Echocardiography cites this paper.

Loss Invariance Determines What Concept Layers Encode: Volume Grounding in Echocardiography Promises and Pitfalls of Black-Box Concept Learning Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T01:40:43.864735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.