Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.01235.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T21:56:28.103866Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T08:19:44.419278Z
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 697a3dbe-9bbf-4765-b1d1-41004303ff33 · inbound
LoRA Training Provably Converges to a Low-Rank Global Minimum or It Fails Loudly (But it Probably Won't Fail) cites this paper.
LoRA Training Provably Converges to a Low-Rank Global Minimum or It Fails Loudly (But it Probably Won't Fail) LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T21:56:28.103866Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4a5746d7-93e9-4521-a468-ba60d607330b · inbound
Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics cites this paper.
Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:04:50.710627Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a3c2d280-9bbc-41f0-88fb-86d9c5507173 · inbound
High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model cites this paper.
High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T12:16:57.382824Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 28f8896a-54b2-4298-94b6-6a50faacb722 · inbound
The Hidden Power of Scaling Factor in LoRA Optimization cites this paper.
The Hidden Power of Scaling Factor in LoRA Optimization LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
Reference 80
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T14:08:21.934456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation fff784f6-0818-4b94-9f82-4a35e93baf58 · inbound
Channel Location Constrains the Auditability of Subliminal Learning cites this paper.
Channel Location Constrains the Auditability of Subliminal Learning LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T08:19:44.420644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8b72c719-4d81-4fd3-983f-4896b623a813 · inbound
Between Gradient and Natural Gradient: A Continuum of LoRA Initializations cites this paper.
Between Gradient and Natural Gradient: A Continuum of LoRA Initializations LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T00:26:41.122870Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.