Paper Citation Record · LEDGER

Robust Semantic Interpretability: Revisiting Concept Activation Vectors

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2104.02768.

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

pith.paper-citation-record.v1
2104.02768 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:43:35.537817Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T17:16:23.148917Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c46df28b-4735-488c-9c99-f545a497c088 · inbound

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks cites this paper.

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks Robust Semantic Interpretability: Revisiting Concept Activation Vectors

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T14:43:35.537817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9db7886-e09c-4564-b06a-391b21f0b4f7 · inbound

Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI cites this paper.

Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI Robust Semantic Interpretability: Revisiting Concept Activation Vectors

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:41:25.390066Z

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 aba6d5c5-201b-4dfe-81e9-e0ad86a5cfc9 · inbound

Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions cites this paper.

Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions Robust Semantic Interpretability: Revisiting Concept Activation Vectors

Reference 239

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:52:58.971541Z

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 bc0bf201-baf9-44fa-85aa-09fd5acd5122 · inbound

Why Fake ? Unveiling the Semantic Vocabulary of Deepfake Detectors cites this paper.

Why Fake ? Unveiling the Semantic Vocabulary of Deepfake Detectors Robust Semantic Interpretability: Revisiting Concept Activation Vectors

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-07-09T17:16:23.151074Z

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.