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Paper Citation Record · LEDGER

Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.08607.

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

pith.paper-citation-record.v1
2406.08607 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 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:44:01.249949Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:25:45.413350Z

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 7187d824-8036-463f-86dc-880087e368ff · inbound

Tool Unlearning for Tool-Augmented LLMs cites this paper.

Tool Unlearning for Tool-Augmented LLMs Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-09T16:44:01.249949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T16:44:01.249949Z digest=sha256:b1cba70052cf1b035bd01449a5f78d1c4afbc443a8bf97a828300c831f91781b

Observation 239c5f2b-a65e-4071-b595-32a44e304a32 · inbound

SoK: Machine Unlearning for Large Language Models cites this paper.

SoK: Machine Unlearning for Large Language Models Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-07T04:58:53.510288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:58:53.510288Z digest=sha256:039306968ce81cc65095b305defc02e04057c7be85dc89c5ce607f04510e5527

Observation 8754f444-b417-4a46-85fc-5d45bb98d51b · inbound

SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion cites this paper.

SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-01T13:25:45.415526Z

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=2026-06-30T23:17:18.978622Z digest=sha256:49f0ff29bb1b5c99adb0cf189017029a25d9525b2604e35aee5a7a9f35719255

Observation 9f1b4716-0afd-4cbe-9ed9-e8e7e91d0d1a · 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 Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference

Reference 104

Resolution
unresolved
no resolver link, observed 2026-08-02T10:25:19.884717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T10:25:19.884717Z digest=sha256:0b12d9c17eb252daf2c3acf7c44a92be561c7ccedca8e692a6f66bd9338569a0

Observation 10382f30-8b51-4862-aa6f-8543dbeb220e · inbound

Understanding Machine Unlearning Through the Lens of Mode Connectivity cites this paper.

Understanding Machine Unlearning Through the Lens of Mode Connectivity Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference

Reference 37

Resolution
unresolved
no resolver link, observed 2026-07-31T23:27:27.090046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T23:27:27.090046Z digest=sha256:75f7df5a2eff00a5d1a1aaccc912fcf81564bb450239281de637a167a68fce30