Paper Citation Record · LEDGER

Gradient dynamics for low-rank fine-tuning beyond kernels

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2411.15385.

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

pith.paper-citation-record.v1
2411.15385 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:26:48.265673Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:42:35.557822Z

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 3eb0d64b-4891-4e21-aa78-e493ce0d0a48 · inbound

Decentralized Low-Rank Fine-Tuning of Large Language Models cites this paper.

Decentralized Low-Rank Fine-Tuning of Large Language Models Gradient dynamics for low-rank fine-tuning beyond kernels

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:48.265673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf0b7bf0-f1c0-48d5-acaf-4b6dc09c3b45 · inbound

LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently cites this paper.

LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently Gradient dynamics for low-rank fine-tuning beyond kernels

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T16:14:02.937714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98c220fe-5550-4078-a9af-aa78bbf0f120 · 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) Gradient dynamics for low-rank fine-tuning beyond kernels

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T21:56:27.937594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 041102b9-2251-4a42-8ab1-c4229f97642f · inbound

Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning cites this paper.

Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning Gradient dynamics for low-rank fine-tuning beyond kernels

Reference 1262

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T11:42:35.567643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.