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Paper Citation Record · LEDGER

Model extraction from counterfactual explanations

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2009.01884.

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

pith.paper-citation-record.v1
2009.01884 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:52:15.653307Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:36:56.363795Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
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  • 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 17be8717-9bf7-471c-833a-d1b36645d317 · inbound

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions cites this paper.

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions Model extraction from counterfactual explanations

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-23T21:55:50.729810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-23T21:54:26.670284Z digest=sha256:5d65b6e71eb94f249f3d67d165cbf60e941a2d3c6860a75dab9b6281cbc3efe4

Observation aa4df42f-3bba-4577-b8f6-a0c38ef18f79 · inbound

Private Counterfactual Retrieval With Immutable Features cites this paper.

Private Counterfactual Retrieval With Immutable Features Model extraction from counterfactual explanations

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-12T19:52:15.653307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:52:15.653307Z digest=sha256:3f0b48c925c6cb08f23bae27bc670b3031d387d1012ed47d61663ab3dd374e50

Observation cc882bcf-8a2f-40b5-9db2-da267dfab097 · inbound

Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models cites this paper.

Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models Model extraction from counterfactual explanations

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T11:14:36.318795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:14:36.318795Z digest=sha256:37f767e3593f719ad930439c43e13981835b8cbe88f4ce69386e4e6d258f8f75

Observation f7ab15cd-c192-47a9-a554-1ad29e0fafb2 · inbound

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives cites this paper.

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives Model extraction from counterfactual explanations

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-05T18:12:33.679833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:12:33.679833Z digest=sha256:7db5cab24eaf5280f5607fca12f1c1a62a971f32fc5fb67ec4849c3c686b1311

Observation b13655e6-6745-4e58-8952-dee027eafbcb · inbound

Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks Against Synthetic Data cites this paper.

Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks Against Synthetic Data Model extraction from counterfactual explanations

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-02T12:36:56.365246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-28T02:03:08.254750Z digest=sha256:8a2839a1f0e10aac95523379116ffb9de17a0d3edd2661739c2fc117f74be1ac