Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2010.12563.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T10:16:38.248004Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T19:50:11.208012Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation 0c8be5a4-0139-462a-95bb-a8e3025f9afb · inbound
RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning Concealed Data Poisoning Attacks on NLP Models
Reference 6
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8f499758-16ba-4934-ae5f-d694e449d34a · inbound
A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation Concealed Data Poisoning Attacks on NLP Models
Reference 31
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 94e3bf5e-1d55-4c90-9ca9-e505211f1e21 · inbound
A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation Concealed Data Poisoning Attacks on NLP Models
Reference 31
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e269dfcf-dbbd-42b0-b2e5-223ba479aa7c · inbound
Pretraining Data Can Be Poisoned through Computational Propaganda Concealed Data Poisoning Attacks on NLP Models
Reference 2021
Source-reported events for the cited work
Unavailable: canonical work link unavailable.