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Paper Citation Record · LEDGER

LoRA Dropout as a Sparsity Regularizer for Overfitting Control

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2404.09610.

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

pith.paper-citation-record.v1
2404.09610 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:43:10.720812Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:08:21.932896Z

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 bbe6b508-73c1-4b3d-bea1-d6cd1d211362 · inbound

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey cites this paper.

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 88

Resolution
verified exact
arxiv_id, observed 2026-05-13T11:32:36.999296Z

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.

source=pdf_text observed=2026-05-13T11:32:36.738536Z digest=sha256:33c4b02b6dcc84071bd4288daf3ace1adde36970a6bb2fe622a93d585b885b7e

Observation 37529008-13dc-4128-81d0-d02cefc3d944 · inbound

ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation cites this paper.

ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-10T14:43:10.720812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:43:10.720812Z digest=sha256:3e41e97859ff2f70243d4cc335b16bf96acf85249cb2ce5e829a81190d419324

Observation 1a791c04-31be-45ec-8a38-b614ad31caa0 · inbound

Memory-Efficient Fine-Tuning of Transformers via Token Selection cites this paper.

Memory-Efficient Fine-Tuning of Transformers via Token Selection LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 4597

Resolution
unresolved
no resolver link, observed 2026-08-09T22:27:57.473492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T22:27:57.473492Z digest=sha256:28e6049995facbf7d2ab324cda304ec56496c6fed25945ab51b293c05d314295

Observation 0a02ca4b-0986-4c10-a5b1-f3a3bbadb8d8 · inbound

Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation cites this paper.

Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T10:18:24.399979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:18:24.399979Z digest=sha256:18a7db7d63b8c60236b96d003021b2627190d6ae2faa43a991700c6311763082

Observation a3cbc716-6122-4f52-9c7d-2bdf49e20e66 · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 200

Resolution
unresolved
no resolver link, observed 2026-08-06T21:36:38.028151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:36:38.028151Z digest=sha256:9f52237249aa76e04b0148577bb46640c79f38efb87d81fe766d757731028485

Observation d3bd287b-246f-4302-82ab-37d7c445573d · inbound

RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding cites this paper.

RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T17:34:05.918085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:34:05.918085Z digest=sha256:29c6e8b5c007f6ecfecddee0b2e7e14b0a0bb2b2e7a2551ade00c48713ae8434

Observation 733922c7-e768-42c4-8661-de32d0abee3d · inbound

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model cites this paper.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-05T20:53:19.543142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:53:19.543142Z digest=sha256:2c70bf1890e8dfe89f1f9cc965b80a0e6ef38205b3477d7d2130736956a8cb67

Observation 30750dc6-100e-4404-9dd6-3bb19de6cbfa · inbound

Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation cites this paper.

Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:41:25.353792Z

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.

source=pdf_text observed=2026-05-18T13:41:05.981163Z digest=sha256:a8d74873fab3570b3bfa33c41c5d4ad5c41ee90c8d8ecc6582ea1f75e1350e87

Observation af254bd9-57ca-49e1-b0f7-5253de3d2a74 · 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 Dropout as a Sparsity Regularizer for Overfitting Control

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-03T09:45:42.953279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:45:42.953279Z digest=sha256:b2130fc8922530495d3e3f47ee9d05d515f3888f0282e187d5f9d7c6bd6be1e3

Observation 69efa364-4044-47c3-aac2-25f379122cc5 · inbound

PureCC: Pure Learning for Text-to-Image Concept Customization cites this paper.

PureCC: Pure Learning for Text-to-Image Concept Customization LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-21T11:10:02.195293Z

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.

source=pdf_text observed=2026-05-21T11:09:15.512162Z digest=sha256:77b12172c53ae460b8552084fbd9b2432474e3e229264e24470dc20f17c66ebb

Observation 5c02c39e-0076-45e3-a55d-c0eb59e54b68 · inbound

The Hidden Power of Scaling Factor in LoRA Optimization cites this paper.

The Hidden Power of Scaling Factor in LoRA Optimization LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 84

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T14:08:21.934172Z

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.

source=arxiv_source observed=2026-06-27T07:14:08.479610Z digest=sha256:1f0b80829989303e1940d6b45cf54cfb907d0a7956adb305415df547f3c5b8fa

Observation 316086ef-5861-4be6-8b90-bccb62e2e1dd · inbound

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery cites this paper.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-04T18:33:56.909201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T18:33:56.909201Z digest=sha256:217769b2a4d0f510c3e3208754b9955e135b82cb079d9886b99f494de76e41c7