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Paper Citation Record · LEDGER

LoRA-GA: Low-Rank Adaptation with Gradient Approximation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2407.05000.

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

pith.paper-citation-record.v1
2407.05000 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:11:02.195801Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:19:44.421796Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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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 16c7bea6-2db8-42e7-8c08-7b110304cfd2 · inbound

HRP: High-Rank Preheating for Superior LoRA Initialization cites this paper.

HRP: High-Rank Preheating for Superior LoRA Initialization LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-08T11:51:10.356322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:51:10.356322Z digest=sha256:2f59bf746392f9ece6a6bba668189112ba3f807f11d15468c3222f8a1b6e1fde

Observation fd9232b5-9df6-49a1-ae56-1b4d57f1c518 · inbound

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits cites this paper.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-08T10:26:45.517130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T10:26:45.517130Z digest=sha256:e1c03f835f610a4e34083f440d8fa43dabd4c417416de079511da3449356916e

Observation 0c63dcc5-bc46-41aa-977e-89b6d0396b97 · inbound

CoLA: Collaborative Low-Rank Adaptation cites this paper.

CoLA: Collaborative Low-Rank Adaptation LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T15:21:55.449383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:55.449383Z digest=sha256:8d6db8e16c00e1c62366e5e69cc3607231b219b92bc5bf5904c3cc07c4308bb5

Observation 6739a88d-d2e1-4775-8820-a5b11a4f267a · inbound

OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models cites this paper.

OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 146

Resolution
unresolved
no resolver link, observed 2026-08-07T14:31:47.518653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:31:47.518653Z digest=sha256:823b37975e2b1b1a5fcedc986c2551d9650e47da5ade0ab24866e45773daeed4

Observation ce71ade5-4c56-4a0e-9682-45c5f43e62a0 · inbound

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints cites this paper.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:54.774002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:48:54.774002Z digest=sha256:a5a42899c4d42884eb6123075b87e0a240a11d436b9151b2cc02021b16bde8d9

Observation 330be462-9aa2-47d5-84d0-1df3afdad9f8 · inbound

HyperAdapt: Simple High-Rank Adaptation cites this paper.

HyperAdapt: Simple High-Rank Adaptation LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:46:25.971700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T13:44:09.263459Z digest=sha256:6e1deb2982e98f07d8a3bf936dd6b19e024c12ceee0c105c3d461e143675f07e

Observation e55e3f5e-b0aa-4b84-88d9-5b2677592d1c · 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-GA: Low-Rank Adaptation with Gradient Approximation

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-03T11:47:19.001908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T11:47:19.001908Z digest=sha256:728563a7ea006718ad9889ddccbb46465c8a01cd38e407025ed00ce61cf8a5f8

Observation af660c30-3218-4d44-b3ba-d44a57b3be09 · inbound

TLoRA+: A Low-Rank Parameter-Efficient Fine-Tuning Method for Large Language Models cites this paper.

TLoRA+: A Low-Rank Parameter-Efficient Fine-Tuning Method for Large Language Models LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-10T14:20:30.230973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T14:18:22.017187Z digest=sha256:e6c3889670a8df9f5c6fd66b82c2ba218dfe05b5c00c23f3732b9191134597df

Observation 340d5d58-483b-4410-8c5f-10937c98d28e · 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-GA: Low-Rank Adaptation with Gradient Approximation

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T08:13:15.245982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T08:08:47.402298Z digest=sha256:dc68cf243f43f539c45a47db787b2af17ebe1b5cbb0dfee3c529f360d3ee8349

Observation 423cf796-4eea-400a-9c21-d5e6ad618682 · inbound

FACT: A Simple and Efficient Framework for Active Finetuning cites this paper.

FACT: A Simple and Efficient Framework for Active Finetuning LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 33

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T22:16:17.174886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T15:28:21.303268Z digest=sha256:e196c2ea64196ce76252d7f719601b986a4129366e2da49be816c448aacb9486

Observation e6056490-1f13-4844-b04f-3a0bc593837c · inbound

Channel Location Constrains the Auditability of Subliminal Learning cites this paper.

Channel Location Constrains the Auditability of Subliminal Learning LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-07-04T08:19:44.424048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T11:52:03.948568Z digest=sha256:57674ba7ffbd81fae25c10b0972ff4ce78ec31facd2e3c5b44990a97fa1459ec

Observation 98c5a593-30cc-440a-9745-9d0a63ddb207 · 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-GA: Low-Rank Adaptation with Gradient Approximation

Reference 244

Resolution
unresolved
no resolver link, observed 2026-08-02T09:51:03.838707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:51:03.838707Z digest=sha256:9109931e9171d9dbbb390efc2cd77ebb7020f2449786025c44327d3a1a4ba2db

Observation 613486fe-4142-4316-9292-2f82f01213ad · inbound

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning cites this paper.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-08T17:11:02.195801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T17:11:02.195801Z digest=sha256:71a55976b9fe13aa81f0d1602b7813c9e79845fa79d39b6824a713223ef5927f