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Paper Citation Record · LEDGER

LLM Unlearning using Gradient Ratio-Based Influence Estimation and Noise Injection

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2508.06467.

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

pith.paper-citation-record.v1
2508.06467 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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T10:25:19.769627Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:05:47.846376Z

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 5cdf8d87-326f-4015-a2d5-004510cc40b5 · inbound

Efficient machine unlearning with minimax optimality cites this paper.

Efficient machine unlearning with minimax optimality LLM Unlearning using Gradient Ratio-Based Influence Estimation and Noise Injection

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:05:47.849051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-10T19:22:43.814805Z digest=sha256:0d2b721c7d03b5aeb5dcfdd6762b28e04b9c2f11e8df49ff42ca918c1788f18e

Observation 5d4ab7a5-449f-4520-8964-e80ce91fdaf0 · inbound

LLM Unlearning for Cyber Defense: A Survey on Methods, Challenges, and Emerging Threats cites this paper.

LLM Unlearning for Cyber Defense: A Survey on Methods, Challenges, and Emerging Threats LLM Unlearning using Gradient Ratio-Based Influence Estimation and Noise Injection

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-02T10:25:19.769627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T10:25:19.769627Z digest=sha256:f6f6ec3e1f0b20efd032af930f3940aa72793e80d07da8992eec7268c84aaf0c