Paper Citation Record · LEDGER

LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2305.18403.

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

pith.paper-citation-record.v1
2305.18403 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:09:30.229964Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T07:16:04.188505Z

Reference resolution

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verified fuzzy0
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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation eece8467-a940-4d30-9a3e-59c99bc509f4 · inbound

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

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 120

Resolution: verified exact; arxiv_id, observed 2026-05-13T11:32:37.137631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 439d121c-82f1-484c-8221-12b03d85c1a2 · inbound

FASTNav: Fine-tuned Adaptive Small-language-models Trained for Multi-point Robot Navigation cites this paper.

FASTNav: Fine-tuned Adaptive Small-language-models Trained for Multi-point Robot Navigation LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T16:43:27.550169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab134f4f-490a-45e5-9508-943d89387983 · inbound

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models cites this paper.

Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T13:56:36.605223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c231864-5e19-4361-a6f6-5c29cf0dd030 · inbound

Not All Adapters Matter: Selective Adapter Freezing for Memory-Efficient Fine-Tuning of Language Models cites this paper.

Not All Adapters Matter: Selective Adapter Freezing for Memory-Efficient Fine-Tuning of Language Models LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T12:27:44.879802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8101fbc1-681a-4d21-a857-884d922595ce · inbound

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs cites this paper.

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T12:17:56.852294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb3df22c-ffb6-41ab-907b-54d2ed26202c · inbound

Adaptive Pruning for Large Language Models with Structural Importance Awareness cites this paper.

Adaptive Pruning for Large Language Models with Structural Importance Awareness LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T11:40:41.677113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86e9b979-4933-4fda-8396-03b73a1673aa · inbound

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference cites this paper.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T11:12:57.952950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43d48f5c-8595-428b-8843-85f19de47dd4 · inbound

SlimGPT: Layer-wise Structured Pruning for Large Language Models cites this paper.

SlimGPT: Layer-wise Structured Pruning for Large Language Models LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T05:05:19.340099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c6b9e71-c560-40b6-b474-1bacc8d39ea0 · inbound

SWSC: Shared Weight for Similar Channel in LLM cites this paper.

SWSC: Shared Weight for Similar Channel in LLM LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T20:26:08.041243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a58e704a-733f-433e-a9ef-3ad4aa58dc74 · inbound

EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models cites this paper.

EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-08T14:48:53.847426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35410915-288d-4501-a21e-73da44c434a7 · inbound

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices cites this paper.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 169

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.475146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3faaae99-1b2a-4eff-a91b-0fd3632e2971 · inbound

RAP: Runtime Adaptive Pruning for LLM Inference cites this paper.

RAP: Runtime Adaptive Pruning for LLM Inference LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.600815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 387bde6f-6707-497d-bb2a-e26386318834 · inbound

Pangu Light: Weight Re-Initialization for Pruning and Accelerating LLMs cites this paper.

Pangu Light: Weight Re-Initialization for Pruning and Accelerating LLMs LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:03:50.150580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f917454-8130-4c47-9291-100440aea80f · inbound

ALTER: All-in-One Layer Pruning and Temporal Expert Routing for Efficient Diffusion Generation cites this paper.

ALTER: All-in-One Layer Pruning and Temporal Expert Routing for Efficient Diffusion Generation LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T13:26:43.815649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc6e8ff4-2f01-4fdd-98d8-d4f976dffa28 · inbound

SlimLLM: Accurate Structured Pruning for Large Language Models cites this paper.

SlimLLM: Accurate Structured Pruning for Large Language Models LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:21.169863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5d46116-a700-4821-9c58-317345c79fdf · inbound

MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs cites this paper.

MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:02:14.375780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dca2cf36-235e-445f-86d1-0f45ecaf35f6 · inbound

Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs cites this paper.

Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T19:09:30.229964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cb09dcf-659f-43c1-b75e-a99915ce1cb3 · inbound

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs cites this paper.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.578456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97d3112d-fc35-4633-8d29-ea6e107af233 · inbound

GradMAP: Faster Layer Pruning with Gradient Metric and Projection Compensation cites this paper.

GradMAP: Faster Layer Pruning with Gradient Metric and Projection Compensation LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 2023

Resolution: malformed identifier; no resolver link, observed 2026-08-02T23:12:07.547170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 998a433b-3a50-4272-b9a8-eb960f3ff038 · inbound

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation cites this paper.

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:06:14.414209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 400c6307-dab2-4c98-9956-0c8bfff0d564 · inbound

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning cites this paper.

PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T07:16:04.191634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 72c9aa9b-2a55-434a-8003-4f91bdf4d635 · inbound

Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems cites this paper.

Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T03:55:41.247033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.