Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.12168.
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 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T14:42:31.558204Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T21:46:35.204845Z
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 dd53e15a-c0e7-48ba-97b1-c0bff164a097 · inbound
Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution cites this paper.
Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:42:31.558204Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a9e253d-44cc-47f1-ba57-954dc7b0063a · inbound
Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates cites this paper.
Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T22:35:52.782863Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 528ff828-b1f3-439b-a2b1-8ca036da4e72 · inbound
Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates cites this paper.
Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T09:41:56.414542Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 995d8c02-d0ec-4ad6-9ed3-e87462ddcb7a · inbound
Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training cites this paper.
Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
Reference 97
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:46:35.222998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.