Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T11:47:19.013126Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T11:47:19.013126Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
27 of 27 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation e1942df3-4318-4716-9c29-19af8278299c · outbound
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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Unavailable: canonical work link unavailable.
Observation 07e53ed3-32cc-468b-9d69-fc6eb781037d · outbound
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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Unavailable: canonical work link unavailable.
Observation bceffdee-e3e1-4c41-8c1b-723a297f3cd8 · outbound
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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Unavailable: canonical work link unavailable.
Observation 487fcc5f-1e92-4ee8-b0c6-fcdd4c193f07 · outbound
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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Observation a27c13db-4b11-4840-8e0c-9480694bbcef · outbound
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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Unavailable: canonical work link unavailable.
Observation 13d570d9-3a83-4207-96f0-631a0e3de7e2 · outbound
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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Unavailable: canonical work link unavailable.
Observation 63db9635-342b-42c6-9623-22c2914d282a · outbound
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Mixtral of Experts
Reference 12
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Unavailable: canonical work link unavailable.
Observation cb4a4133-1aa6-49b9-9167-76d301d32700 · outbound
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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Unavailable: canonical work link unavailable.
Observation dbbe116f-5986-4e9b-851e-916a318179a5 · outbound
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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Unavailable: canonical work link unavailable.
Observation f460d260-4f8b-4ac8-975d-e2dcc2e462b1 · outbound
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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Unavailable: canonical work link unavailable.
Observation 70240f9c-be65-4733-a243-bc25adf70fb6 · outbound
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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Unavailable: canonical work link unavailable.
Observation b50acfb4-faf9-482d-b512-a90c432d1e82 · outbound
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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Unavailable: canonical work link unavailable.
Observation 49e2b9d0-afac-44c5-b61e-7e65bf53e567 · outbound
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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Unavailable: canonical work link unavailable.
Observation ab9538ff-bd48-4d6c-bde3-346ec3751bbb · outbound
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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Unavailable: canonical work link unavailable.
Observation 14a0358b-3384-4c6e-9d8e-236a936f55b0 · outbound
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models GPT-4 Technical Report
Reference 20
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Unavailable: canonical work link unavailable.
Observation 505ab54c-14aa-4e74-a3b9-fadeb63b9cde · outbound
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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Unavailable: canonical work link unavailable.
Observation 8fead918-b3db-4ff9-a4dc-300b453ef88c · outbound
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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Unavailable: canonical work link unavailable.
Observation e55e3f5e-b0aa-4b84-88d9-5b2677592d1c · outbound
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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Unavailable: canonical work link unavailable.
Observation aa94d0d5-9420-4ada-868f-ae926a198e00 · outbound
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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Unavailable: canonical work link unavailable.
Observation f690e976-331a-45ab-a40b-0650d905e719 · outbound
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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Unavailable: canonical work link unavailable.
Observation eb8a0118-c2d0-412d-a04b-8d700c39c3c6 · outbound
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models Qwen3 Technical Report
Reference 26
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Unavailable: canonical work link unavailable.
Observation f796b6cc-370a-44bb-b192-68665ea90df1 · outbound
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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Unavailable: canonical work link unavailable.
Observation b74b6600-58a3-47ad-9cbb-f65b496f9671 · outbound
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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Unavailable: canonical work link unavailable.
Observation ad67ffc0-bcc1-4ac2-8b56-d8561be6ac55 · outbound
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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Unavailable: canonical work link unavailable.
Observation 2a46afe7-0550-47c3-b2ad-d7e6881fc708 · outbound
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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Observation bf33f51c-f033-4e76-a1ff-2fc64f2a370c · outbound
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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Unavailable: canonical work link unavailable.
Observation a2f7631e-4626-4ba4-b622-9ec015d7eec1 · outbound
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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No inbound Pith citation observations are available.