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Paper Citation Record · LEDGER

Exploring Connections Between Active Learning and Model Extraction

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1811.02054.

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

pith.paper-citation-record.v1
1811.02054 v6

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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:27:49.996877Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T10:55:08.283764Z

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 32079bf1-b88a-4275-8193-87180d446445 · inbound

High Accuracy and High Fidelity Extraction of Neural Networks cites this paper.

High Accuracy and High Fidelity Extraction of Neural Networks Exploring Connections Between Active Learning and Model Extraction

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-14T05:27:49.996877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T05:27:49.996877Z digest=sha256:e588eb25d5ac926686b65bb7e5c0d886f9f3994ba21f6043661d30b14c8d1d6b

Observation d6931319-a011-4f51-9486-45fcd31df05e · inbound

When Do PEFT Adaptations Leak Structure? Measuring Black-Box Structural Bounds in Public-Base Model Services cites this paper.

When Do PEFT Adaptations Leak Structure? Measuring Black-Box Structural Bounds in Public-Base Model Services Exploring Connections Between Active Learning and Model Extraction

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-06T10:55:08.291132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T10:55:07.956609Z digest=sha256:aa5d315be6b0136bb65c65cdc62dc0e2a545b8df0b7c8f1ed6d8b3d6157d2b95