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Paper Citation Record · LEDGER

AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.16776.

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

pith.paper-citation-record.v1
2502.16776 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 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T07:06:46.387088Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T07:07:27.052174Z

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 85c9e726-8bea-48ba-b7ce-3018e14f2bbe · inbound

SoK: Robustness in Large Language Models against Jailbreak Attacks cites this paper.

SoK: Robustness in Large Language Models against Jailbreak Attacks AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement

Reference 99

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T18:01:08.950687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-08T16:42:41.137808Z digest=sha256:8057f7103d0ed6cffb1b12e7fe6d4ec96d8a4f469ea7cd75c01b663aaf586963

Observation 26e229f8-e41d-46a1-b29f-9ba9e6474e7b · inbound

Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs cites this paper.

Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:07:27.056559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-13T07:06:46.387088Z digest=sha256:3c1dadc527deef15828c6d4f38d38bee340cd0eb01e8157300772ebcfdc277a3