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Paper Citation Record · LEDGER

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models

As of 9 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2601.16991.

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

pith.paper-citation-record.v1
2601.16991 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T11:47:19.013126Z

measured 27 of 27 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

27 of 27 outbound references displayed

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External citation measurements

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Outbound references

Observation e1942df3-4318-4716-9c29-19af8278299c · outbound

This paper cites OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models

Reference 2

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source=pdf_text observed=2026-08-03T11:47:18.941572Z digest=sha256:4e97ac57998d0a63025565a988c6d3c88a640d86aa1bcc6b7479ee31dcc40b51

Observation 07e53ed3-32cc-468b-9d69-fc6eb781037d · outbound

This paper cites The Llama 3 Herd of Models.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models The Llama 3 Herd of Models

Reference 6

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source=pdf_text observed=2026-08-03T11:47:18.954020Z digest=sha256:20713d2f462271368e3bcbcf8d534f967082eb17b0c9bd2c2a23bcfc304d018e

Observation bceffdee-e3e1-4c41-8c1b-723a297f3cd8 · outbound

This paper cites SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 7

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source=pdf_text observed=2026-08-03T11:47:18.957236Z digest=sha256:76990611ad000810c6f19dfaa8794e574513b56e58784a3ff27b19255e08f915

Observation 487fcc5f-1e92-4ee8-b0c6-fcdd4c193f07 · outbound

This paper cites Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models

Reference 8

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source=pdf_text observed=2026-08-03T11:47:18.960006Z digest=sha256:0be62c0463f2c69b314a2f1d66f0b13a55faf9765577468077c451d29f280dbd

Observation a27c13db-4b11-4840-8e0c-9480694bbcef · outbound

This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 10

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source=pdf_text observed=2026-08-03T11:47:18.965575Z digest=sha256:146f248ac9ccd93c9398389ade9145dde81544af614a7f6bed2868822a8d8179

Observation 13d570d9-3a83-4207-96f0-631a0e3de7e2 · outbound

This paper cites Dynamic Low-Rank Sparse Adaptation for Large Language Models.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Dynamic Low-Rank Sparse Adaptation for Large Language Models

Reference 11

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source=pdf_text observed=2026-08-03T11:47:18.968474Z digest=sha256:78befbe71ccaa0a109d5620f68f7f195c653d4350efb2b6df3d397ab56552c2a

Observation 63db9635-342b-42c6-9623-22c2914d282a · outbound

This paper cites Mixtral of Experts.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Mixtral of Experts

Reference 12

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source=pdf_text observed=2026-08-03T11:47:18.971307Z digest=sha256:6ac31e9d4f4df82057899d75dca45794850117fd0367650179e97eccb6b39d0c

Observation cb4a4133-1aa6-49b9-9167-76d301d32700 · outbound

This paper cites SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity

Reference 13

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source=pdf_text observed=2026-08-03T11:47:18.974003Z digest=sha256:c5896dfacf762189279e54843ab830caab4a216a013ff1aca1f681f0e5148e3f

Observation dbbe116f-5986-4e9b-851e-916a318179a5 · outbound

This paper cites Sparse Fine-tuning for Inference Acceleration of Large Language Models.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Sparse Fine-tuning for Inference Acceleration of Large Language Models

Reference 14

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source=pdf_text observed=2026-08-03T11:47:18.976787Z digest=sha256:91b8285d8c45b57a0cd5fd9f3dd33d97a2d0058733df5c893d4b9e592697e66c

Observation f460d260-4f8b-4ac8-975d-e2dcc2e462b1 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 15

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source=pdf_text observed=2026-08-03T11:47:18.979637Z digest=sha256:503e5517a9ac5730208a4d40e8b0e6abd2f4903d844e46a330424bbe976a1638

Observation 70240f9c-be65-4733-a243-bc25adf70fb6 · outbound

This paper cites SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation

Reference 16

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source=pdf_text observed=2026-08-03T11:47:18.982394Z digest=sha256:c74c59b39755cd959ad410faddd4109b07654b7cdfe62592e61783c16e95d031

Observation b50acfb4-faf9-482d-b512-a90c432d1e82 · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 17

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source=pdf_text observed=2026-08-03T11:47:18.985173Z digest=sha256:5f067a578e01410157ae8dbced951fd837b46b0ca338a7539fffaa7a49b0e378

Observation 49e2b9d0-afac-44c5-b61e-7e65bf53e567 · outbound

This paper cites PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

Reference 18

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source=pdf_text observed=2026-08-03T11:47:18.987891Z digest=sha256:4b7b3657095bf3d6f7160fd7db0cb8e8ad1eec0ccdbbf639a6d365e62926bc93

Observation ab9538ff-bd48-4d6c-bde3-346ec3751bbb · outbound

This paper cites https://ai.meta.com/blog/ llama-4-multimodal-intelligence/ [Accessed: 2025-04-05].

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models https://ai.meta.com/blog/ llama-4-multimodal-intelligence/ [Accessed: 2025-04-05]

Reference 19

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source=pdf_text observed=2026-08-03T11:47:18.990878Z digest=sha256:755ec0e545702fd86578cadf9c5f24b8f9e12b7d45782287d341db9f83321b7d

Observation 14a0358b-3384-4c6e-9d8e-236a936f55b0 · outbound

This paper cites GPT-4 Technical Report.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models GPT-4 Technical Report

Reference 20

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source=pdf_text observed=2026-08-03T11:47:18.993568Z digest=sha256:b1e0a1112cb28b1532ceca3b3320b419fb6bda47ed4be34cbe5956bdfa037350

Observation 505ab54c-14aa-4e74-a3b9-fadeb63b9cde · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models A Simple and Effective Pruning Approach for Large Language Models

Reference 21

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source=pdf_text observed=2026-08-03T11:47:18.996406Z digest=sha256:d54ce6ead0d954f3cc1e7541db359ed5249bb974cfb2de1afe5cc4810bb03e13

Observation 8fead918-b3db-4ff9-a4dc-300b453ef88c · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 22

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source=pdf_text observed=2026-08-03T11:47:18.999183Z digest=sha256:0f87136a99702fe4500e69ee8a38950300ff6275442c52d3cc1cc29783d587b2

Observation e55e3f5e-b0aa-4b84-88d9-5b2677592d1c · outbound

This paper cites LoRA-GA: Low-Rank Adaptation with Gradient Approximation.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 23

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

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

This paper cites LoRA-Pro: Are Low-Rank Adapters Properly Optimized?.

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

Reference 24

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source=pdf_text observed=2026-08-03T11:47:19.004644Z digest=sha256:449782149d1a5d43f9b3c2a7b7fe3419140d20a574f54d71a1d76b5239353502

Observation f690e976-331a-45ab-a40b-0650d905e719 · outbound

This paper cites Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity

Reference 25

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source=pdf_text observed=2026-08-03T11:47:19.007455Z digest=sha256:81eca4857cdcdd15f665167ae76960b1ae3da5aee9f1538a07a654954cd08a6f

Observation eb8a0118-c2d0-412d-a04b-8d700c39c3c6 · outbound

This paper cites Qwen3 Technical Report.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Qwen3 Technical Report

Reference 26

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source=pdf_text observed=2026-08-03T11:47:19.010255Z digest=sha256:95d102e7cf3959af21721caeacbec998118b0aca0d429648a964e50f486c7cf7

Observation f796b6cc-370a-44bb-b192-68665ea90df1 · outbound

This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 27

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source=pdf_text observed=2026-08-03T11:47:19.013126Z digest=sha256:f23be77eb5f43bc3190cbd8188511107b1d8db26d7da5cbb7a9c8fe28c199a62

Observation b74b6600-58a3-47ad-9cbb-f65b496f9671 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 2016

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source=pdf_text observed=2026-08-03T11:47:18.962735Z digest=sha256:992950c19c7c298f9ca5218bf7a5c1f4008a8c1e58091173a1abbb68ae840362

Observation ad67ffc0-bcc1-4ac2-8b56-d8561be6ac55 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Training Verifiers to Solve Math Word Problems

Reference 2021

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source=pdf_text observed=2026-08-03T11:47:18.944765Z digest=sha256:a18b10ba6c6e4f5c95c67be2172c6553cdaaf13a84e8f9ee49874c478eab2969

Observation 2a46afe7-0550-47c3-b2ad-d7e6881fc708 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models QLoRA: Efficient Finetuning of Quantized LLMs

Reference 2023

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source=pdf_text observed=2026-08-03T11:47:18.950879Z digest=sha256:a567e7598f3672fc665d060749319430dab1f754c279da53b2f828c8d2d87c27

Observation bf33f51c-f033-4e76-a1ff-2fc64f2a370c · outbound

This paper cites Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment

Reference 2024

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source=pdf_text observed=2026-08-03T11:47:18.937690Z digest=sha256:e07e08eb8b01f0c75eea241df76922abed9c0fd4e7e92ce93c09252d53f95257

Observation a2f7631e-4626-4ba4-b622-9ec015d7eec1 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2025

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source=pdf_text observed=2026-08-03T11:47:18.947899Z digest=sha256:bc2ac5cb6337b9b6cd4efda1b5a56e0f316a1a7fdf1e9190933915b46ee7cd5f

Pith citing papers

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