Paper Citation Record · LEDGER

Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2402.10376.

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

pith.paper-citation-record.v1
2402.10376 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:26:35.474834Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T02:55:19.737143Z

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 d1ebb410-891d-4abb-a590-22be1b33fff7 · inbound

Explainability for Vision Foundation Models: A Survey cites this paper.

Explainability for Vision Foundation Models: A Survey Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

Reference 132

Resolution: unresolved; no resolver link, observed 2026-08-10T17:26:35.474834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e24d8c85-59a2-4ef4-b75a-56614f039731 · inbound

Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models cites this paper.

Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:55:19.739238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5b7508f2-a33c-44c2-9209-593e178dffe3 · inbound

Quantifying Structure in CLIP Embeddings: A Statistical Framework for Concept Interpretation cites this paper.

Quantifying Structure in CLIP Embeddings: A Statistical Framework for Concept Interpretation Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T00:46:32.566316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d092a9c8-e782-4bf8-9063-2ac28c24e2ce · inbound

Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual Descriptor cites this paper.

Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual Descriptor Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T20:13:45.126162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 650bd057-0edb-4670-908b-4c980c962b1b · inbound

Model Science: getting serious about verification, explanation and control of AI systems cites this paper.

Model Science: getting serious about verification, explanation and control of AI systems Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T15:17:08.525032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42675fb1-4117-4ba7-a699-37027d9c2c01 · inbound

Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach cites this paper.

Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-18T02:00:39.992703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 95269639-066c-4c2c-9a44-7c6451f8d7bb · inbound

Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach cites this paper.

Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T00:23:29.812352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf7125b6-8178-4fc8-a963-6e3876b2cf33 · inbound

Zero-Shot Textual Explanations via Translating Decision-Critical Features cites this paper.

Zero-Shot Textual Explanations via Translating Decision-Critical Features Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-21T18:10:27.636126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 64c01702-8c85-4c30-a626-dff2d32539d6 · inbound

The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model cites this paper.

The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T04:33:52.123264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fc961ab-4d6f-4d86-bfe5-bc02fa66dd53 · inbound

Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models cites this paper.

Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)

Reference 2

Resolution: malformed identifier; no resolver link, observed 2026-08-01T14:41:33.468879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.