Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.12274.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T19:40:31.573098Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-08T17:43:25.105682Z
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 2d4d4c01-9df2-43ef-90d3-3fdd4b514194 · inbound
Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond cites this paper.
Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:40:31.573098Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54f4aaac-de99-42b2-9c0f-2bc31ad5c99f · inbound
Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks cites this paper.
Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-08-08T17:43:25.115510Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.