Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.16833.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T23:10:21.190493Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T20:58:26.247370Z
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 7feaade8-75e4-49bd-bddd-a2e639dc9b9b · inbound
Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey cites this paper.
Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
Reference 54
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T20:58:26.252024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation dbf4c8b7-f714-4c6f-a26b-2a8f31aea69f · inbound
Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint cites this paper.
Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T23:10:21.190493Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ef3557ae-8039-45ce-8b44-83a2979f2965 · inbound
MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security cites this paper.
MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T23:09:41.491116Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18bcd5c1-1aa6-4df8-a04d-8f3ed6d97f5a · inbound
LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems cites this paper.
LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
Reference 124
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T17:46:19.311164Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7ccab32-557c-4d6b-ab7d-541fe2f73b7c · inbound
SafeAnchor: Preventing Cumulative Safety Erosion in Continual Domain Adaptation of Large Language Models cites this paper.
SafeAnchor: Preventing Cumulative Safety Erosion in Continual Domain Adaptation of Large Language Models Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T05:25:55.123958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.