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Paper Citation Record · LEDGER

Stealing Training Data from Large Language Models in Decentralized Training through Activation Inversion Attack

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.16086.

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

pith.paper-citation-record.v1
2502.16086 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 2026-08-09T21:58:40.192468Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T22:23:16.775857Z

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 49b7f45c-e54c-457a-aef2-b968b94dab59 · inbound

Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey cites this paper.

Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey Stealing Training Data from Large Language Models in Decentralized Training through Activation Inversion Attack

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-09T21:58:40.192468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:58:40.192468Z digest=sha256:136bdbeed054c4b281c453472366d920ea2dc712b59d984bbed819d4d4b71d80

Observation 82c264c8-4685-4d0f-b45d-e6a6f3417981 · inbound

A Survey on Model Extraction Attacks and Defenses for Large Language Models cites this paper.

A Survey on Model Extraction Attacks and Defenses for Large Language Models Stealing Training Data from Large Language Models in Decentralized Training through Activation Inversion Attack

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-08-06T22:23:16.820213Z

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=2026-08-06T22:23:07.990975Z digest=sha256:84acc648aeb391e96d5188bb17dc8b94f7bc0f5414630549ea840eac2fd112ae