Paper Citation Record · LEDGER

LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2407.18242.

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

pith.paper-citation-record.v1
2407.18242 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:21:55.493833Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:08:43.688765Z

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 974fff51-dbed-46ba-8dca-3dbd0e61f04d · inbound

CoLA: Collaborative Low-Rank Adaptation cites this paper.

CoLA: Collaborative Low-Rank Adaptation LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:55.493833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b3c9b55-fafd-47f1-be7c-bc175f2d3d52 · inbound

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking cites this paper.

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:26.785017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa94d0d5-9420-4ada-868f-ae926a198e00 · inbound

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models cites this paper.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T11:47:19.004644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5583a457-859c-42bb-ac3a-a117bdbfce8f · inbound

A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search cites this paper.

A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T09:45:44.992091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bea19539-a82e-4e90-8e3f-2e003929e74d · inbound

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics cites this paper.

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 81

Resolution: unresolved; no resolver link, observed 2026-07-13T14:28:05.261852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3e82a0f-099e-43ee-93ab-7a8cc389e12c · inbound

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics cites this paper.

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 81

Resolution: unresolved; no resolver link, observed 2026-07-15T11:44:19.622453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41b27c43-c9f7-4274-9671-ac9bc80139a7 · inbound

Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization cites this paper.

Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:26:27.674917Z

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.

Observation f52cff11-8db5-456c-8364-a1b4b757538a · inbound

SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning cites this paper.

SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:26:25.261105Z

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.

Observation cef67f42-18ef-4f39-8939-065c87100101 · inbound

FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation cites this paper.

FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:13:15.257033Z

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.

Observation bd4be7e2-3adc-4bf8-84a4-ef5391651ea5 · inbound

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning cites this paper.

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:08:43.690183Z

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.

Observation a6cea29f-d083-4d55-85be-e5566d490e98 · inbound

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling cites this paper.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

Reference 245

Resolution: unresolved; no resolver link, observed 2026-08-02T09:51:03.840772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.