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Paper Citation Record · LEDGER

Concept Embedding Analysis: A Review

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2203.13909.

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

pith.paper-citation-record.v1
2203.13909 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T05:46:57.347062Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T17:51:54.913483Z

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 b2acb5fd-85b4-4211-9a93-d181bb535560 · inbound

If Concept Bottlenecks are the Question, are Foundation Models the Answer? cites this paper.

If Concept Bottlenecks are the Question, are Foundation Models the Answer? Concept Embedding Analysis: A Review

Reference 70

Resolution
verified exact
arxiv_id, observed 2026-05-22T17:51:54.916022Z

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.

source=pdf_text observed=2026-05-22T17:50:46.539215Z digest=sha256:86a57e837172e962944946b802ff0fafd3313f2e5539c55b60baa824e9e315ad

Observation 6ef1ad98-f73f-4b3b-8eaf-6ae6a6d4cdf7 · inbound

Geo-Spatial Concept Probing of Large Language Models: Abstraction, Compositionality, and Grounding cites this paper.

Geo-Spatial Concept Probing of Large Language Models: Abstraction, Compositionality, and Grounding Concept Embedding Analysis: A Review

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-10T05:46:57.347062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T05:46:57.347062Z digest=sha256:99d5cf46a18c381435b4159bc4e3de50a6210e7725d9a4e6fc99be41fceaa1e3