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Paper Citation Record · LEDGER

Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models

As of 9 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 2026-08-09T06:31:02.800959+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:d29db6faed681d26da2f03f25befb4e38f0b1287ec3e64b9645e995479dae915

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T20:07:38.811639Z digest=sha256:b1c8213e86555d32d9948b95b29d65b164527a644ce0d354f85f1c595a8bd310

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T05:52:40.242207Z digest=sha256:334705c9049a0112a867963e084e44e35b10afd3679aee8afbca7004234de37c

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T05:07:39.558349Z digest=sha256:fa1b62251cd16170bbf56a584ec53dcef39040fff39f08a6e49becd9f1d4ffa1

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:f30a0333c741e1639994aee3b291ffa5c643791dc2a870f11b50344f5affcc6d

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=sha256:3e936c2193997ecba3495ab955916fddb18991842c8ebad5bdf60bf39b3f73d5