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Paper Citation Record · LEDGER

Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.02347.

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

pith.paper-citation-record.v1
2402.02347 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:38:17.133482Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T20:10:18.151474Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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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 14d0e14a-b491-4839-8a4f-e0fa816ac1e1 · inbound

SingLoRA: Low Rank Adaptation Using a Single Matrix cites this paper.

SingLoRA: Low Rank Adaptation Using a Single Matrix Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T19:38:17.133482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:38:17.133482Z digest=sha256:94c828aeb5c5404a2ad34f5c3b28fc2afe8e507bac214cb178f967e6627a9b5f

Observation 705376b7-9f31-4493-a940-0947ca07dc4e · inbound

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models cites this paper.

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-15T20:10:18.154825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-15T20:07:38.811639Z digest=sha256:89c32107f20c185718668209459250a06fcb0b881827b7c0a937b760f9926738

Observation bc9a28ff-d8bd-4163-baa5-b731b91a1b27 · inbound

A Retraction-Free EXTRA Method for Decentralized Optimization on the Stiefel Manifold cites this paper.

A Retraction-Free EXTRA Method for Decentralized Optimization on the Stiefel Manifold Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:21:11.378843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T05:52:40.242207Z digest=sha256:7717ea8591bbb5d476c9bbcd60c6d63409ed36e53de90748d39eb9a0ecf18223

Observation 2f3b36b2-ceaa-40a6-a9e6-a04955b5a51a · inbound

Intrinsic Muon: Spectral Optimization on Riemannian Matrix Manifolds cites this paper.

Intrinsic Muon: Spectral Optimization on Riemannian Matrix Manifolds Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models

Reference 70

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:36:26.261092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-12T05:07:39.558349Z digest=sha256:ea873dad6ab6bd66679bad7b25ee1f9fd58478ea5e7a29037f787e5862bd9a2b

Observation 7e52db91-8d00-4cf5-9908-c741ef90aabd · inbound

ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System cites this paper.

ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-02T03:41:13.457236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:41:13.457236Z digest=sha256:b0cf6ac2c0e5305922e4a49ec62a3892eb5c1e679dcb9082f44e0075620a9051

Observation c32bdcd8-71e2-4f3c-8759-8a077f3e5d3f · inbound

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection cites this paper.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.824474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.824474Z digest=sha256:364d5a59d210e7fa4fe56837c7b7b5825f03f4b249d4b03d27158307a3859ea7