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Paper Citation Record · LEDGER

Gradient Surgery for Safe LLM Fine-Tuning

As of 19 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 4 inbound Pith citation observations for arXiv:2508.07172.

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

pith.paper-citation-record.v1
2508.07172 v1

Coverage vector

measured 2 of 2 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:21:39.095042Z

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T12:04:13.336341Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T21:56:15.987326Z

Reference resolution

2 of 2 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69cd2309-9341-4780-a8a3-bd57cb76bb36 · outbound

This paper cites SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging.

Gradient Surgery for Safe LLM Fine-Tuning SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-05T22:21:39.095042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:21:39.095042Z digest=sha256:18776947dd0600e8ac00da71a89af96500c2a283b3bca33685bc337bb92fc01a

Observation 6c03869a-dafc-476d-86aa-c11aebb3b08c · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Gradient Surgery for Safe LLM Fine-Tuning Training Verifiers to Solve Math Word Problems

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-05T22:21:39.091517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:21:39.091517Z digest=sha256:4714d4750cee86536d0217f4eeb63c7a4a9d3737ef2a8cd4f452d9f8fa9f2a87

Pith citing papers

Observation b47801cc-3742-469e-a375-0b68f43005aa · inbound

CURE:Circuit-Aware Unlearning for LLM-based Recommendation cites this paper.

CURE:Circuit-Aware Unlearning for LLM-based Recommendation Gradient Surgery for Safe LLM Fine-Tuning

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-13T16:48:02.737914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-13T16:47:25.256063Z digest=sha256:36f5e521ace1bb90957c585a2d4af1f3a9a78a71c48dd5877e7eeccadc9334c0

Observation 0b2cdec6-439c-4560-804c-270d90524470 · inbound

A Numerical PDEs Approach to Evolution Equations in Shape Analysis Based on Regularized Morphoelasticity cites this paper.

A Numerical PDEs Approach to Evolution Equations in Shape Analysis Based on Regularized Morphoelasticity Gradient Surgery for Safe LLM Fine-Tuning

Reference 38

Resolution
unresolved
no resolver link, observed 2026-07-13T12:04:13.336341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T12:04:13.336341Z digest=sha256:e7a8781d560d9c6415e3e18817f33f45c5fbf3b7c2ac59bc6bef903b6018164b

Observation c0b18a71-6e69-46ef-aa2a-bb87d47ea228 · inbound

SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection cites this paper.

SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection Gradient Surgery for Safe LLM Fine-Tuning

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T13:53:28.675623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-29T13:49:56.311711Z digest=sha256:cd607fa3d5e1c7789a96ff9848f7851c8f63744a9bcb965120625e9f66c14b6e

Observation eb57267f-6efd-4f4e-a55c-dc5f489e2acc · inbound

Two to Tango: Coupled Task-Reference Selection for Safe LLM Fine-tuning cites this paper.

Two to Tango: Coupled Task-Reference Selection for Safe LLM Fine-tuning Gradient Surgery for Safe LLM Fine-Tuning

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-01T21:56:15.989152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-28T15:58:55.037594Z digest=sha256:a26f5f08d9773f3887ead55d12e076dcb3339cb66b72166ecdcc046051df32cb