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Paper Citation Record · LEDGER

FiMMIA: scaling semantic perturbation-based membership inference across modalities

As of 23 August 2026, this Paper Citation Record lists 8 of 8 outbound references and 0 inbound Pith citation observations for arXiv:2512.02786.

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

pith.paper-citation-record.v1
2512.02786 v1

Coverage vector

measured 8 of 8 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:00:46.849166Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

8 of 8 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c0064418-74ce-44bf-a5bb-81385ba1814c · outbound

This paper cites How much do language models memorize?.

FiMMIA: scaling semantic perturbation-based membership inference across modalities How much do language models memorize?

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-03T19:00:46.825180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:00:46.825180Z digest=sha256:0e041ed06b5735b0dd3fe6cbc636c8c0b0ec9be6deaee8faf5bce68e05cacc06

Observation a32d5988-584d-4f29-9cb3-540f7386a92a · outbound

This paper cites Benchmarking Benchmark Leakage in Large Language Models.

FiMMIA: scaling semantic perturbation-based membership inference across modalities Benchmarking Benchmark Leakage in Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-03T19:00:46.849166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:00:46.849166Z digest=sha256:78b6c42bb1b9fe98bcf148bae6b4eaab7ebac4491cf694a2538416e9f5f9df9e

Observation 43bbcf05-6b4b-4d2d-bd71-387812c38152 · outbound

This paper cites Oscar Sainz, Jon Campos, Iker García-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, and Eneko Agirre.

FiMMIA: scaling semantic perturbation-based membership inference across modalities Oscar Sainz, Jon Campos, Iker García-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, and Eneko Agirre

Reference 1144

Resolution
unresolved
no resolver link, observed 2026-08-03T19:00:46.831519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:00:46.831519Z digest=sha256:62cd8cf41b46f19b59c942c3f1c645744337a814e71be0c50553ebab6efc38e0

Observation c4387452-d446-437c-95c1-29029068caa1 · outbound

This paper cites Are We on the Right Way for Evaluating Large Vision-Language Models?.

FiMMIA: scaling semantic perturbation-based membership inference across modalities Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-03T19:00:46.159905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:00:46.159905Z digest=sha256:4d28c2a500d261b666fb9399c1bc22a5ea13e248d97bb3ff4b7bac29d8047840

Observation 319d2859-e15c-4075-b5ee-b30da86da3a1 · outbound

This paper cites InProceed- ings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1816–1826.

FiMMIA: scaling semantic perturbation-based membership inference across modalities InProceed- ings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1816–1826

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-03T19:00:46.820680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:00:46.820680Z digest=sha256:9db0fae012092fa830f2fc763b2a8bc0ac454a88a1809be9ebd2d8fc3cb4b310

Observation e41bf3f0-2017-4bf5-ab10-4976be732e0b · outbound

This paper cites ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.

FiMMIA: scaling semantic perturbation-based membership inference across modalities ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-03T19:00:46.836864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:00:46.836864Z digest=sha256:3d12935a6eee2b01710467924335dc5237951169f17e2dfa73df7968ead3856c

Observation 961b826f-32be-4b23-9f41-4ec2ea01b5bb · outbound

This paper cites Qwen2-Audio Technical Report.

FiMMIA: scaling semantic perturbation-based membership inference across modalities Qwen2-Audio Technical Report

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-03T19:00:46.814436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:00:46.814436Z digest=sha256:65b93a361bace9fda9d741939c79b9f60f9cf565e910cd714889a22bfa91218a

Observation c406edaa-751a-4ce6-a6cb-4add38b52bfc · outbound

This paper cites an unresolved cited work.

FiMMIA: scaling semantic perturbation-based membership inference across modalities Unresolved cited work

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-03T19:00:46.843286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:00:46.843286Z digest=sha256:8395f98c3f77c7c2e8e5353b81ecdd19812f15f6b1708011136756a1f38681c3

Pith citing papers

No inbound Pith citation observations are available.