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Paper Citation Record · LEDGER

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs

As of 15 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2502.00899.

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

pith.paper-citation-record.v1
2502.00899 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:26:45.574894Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T11:31:25.851340Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:46:26.859702Z

Reference resolution

55 of 55 outbound references displayed

  • verified exact2
  • verified fuzzy17
  • unresolved35
  • parse uncertain0
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External citation measurements

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Outbound references

Observation f1439142-1051-4e61-ba21-f77016bd0ae6 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 1

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Observation e099c6f5-8877-4b7d-ba62-4882d492ac61 · outbound

This paper cites GPT-4 Technical Report.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs GPT-4 Technical Report

Reference 2

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source=pdf_text observed=2026-08-09T17:26:45.276912Z digest=sha256:3f5d70614fa48c4185029f0c819fb4b3b1145675ba82008ea1eead6045ecd04a

Observation 9ca15712-873b-4c62-8e57-3b109eba97cd · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Gemini: A Family of Highly Capable Multimodal Models

Reference 3

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Observation 9c00a883-6086-48df-af66-d11440325053 · outbound

This paper cites The Llama 3 Herd of Models.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs The Llama 3 Herd of Models

Reference 4

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source=pdf_text observed=2026-08-09T17:26:45.291926Z digest=sha256:a2108191c79bcead99b9fcf81776e2beba8093972df598f159c5c3b8ea06fbda

Observation 70c8129f-58ee-4186-8f30-dc1785104ec4 · outbound

This paper cites Optimalbraindamage.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Optimalbraindamage

Reference 5

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 323dcebf-9bec-4c47-ace1-f437af4adef9 · outbound

This paper cites Second order derivatives for network pruning: Optimal brain surgeon.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Second order derivatives for network pruning: Optimal brain surgeon

Reference 6

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Observation 15e43a56-2896-4d00-bea4-3c881ecc3b68 · outbound

This paper cites Fast as CHITA: Neural Network Pruning with Combinatorial Optimization.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Fast as CHITA: Neural Network Pruning with Combinatorial Optimization

Reference 7

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source=pdf_text observed=2026-08-09T17:26:45.311804Z digest=sha256:755f007a059c546270e8ab1c3c134f376eed2254611ae2ce968f8e1fc7db0848

Observation 73a3de7b-f005-4b0e-bc2e-f1c800d8a543 · outbound

This paper cites Fast convnets using group-wise brain damage.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Fast convnets using group-wise brain damage

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 4b003056-7afd-456e-aa83-acb52c30bb98 · outbound

This paper cites Learning structured sparsity in deep neural networks.Advances in neural information processing systems, 29, 2016.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Learning structured sparsity in deep neural networks.Advances in neural information processing systems, 29, 2016

Reference 9

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source=pdf_text observed=2026-08-09T17:26:45.327737Z digest=sha256:0484a4e44aa8c429588d6cb30b779928bd1d427f9e0e8004ebda09d1b5291cae

Observation 5dc1e382-f00a-4c4a-ab89-6cbfd2520ae9 · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 10

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Observation 070e3a14-6475-407e-be07-41bf0fc81c24 · outbound

This paper cites Data-efficient structured pruning viasubmodularoptimization.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Data-efficient structured pruning viasubmodularoptimization

Reference 11

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 7713b949-1a52-4986-a618-818996e4d07a · outbound

This paper cites Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch

Reference 12

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Observation b73da1c0-1a38-47a8-953f-4dd0bc6896cd · outbound

This paper cites Learning both weights and connections for efficient neural network.Advances in neural information processing systems, 28, 2015.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Learning both weights and connections for efficient neural network.Advances in neural information processing systems, 28, 2015

Reference 13

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Observation f29c5dd0-8e6f-459e-b947-27a7f757362d · outbound

This paper cites Dynamic network surgery for efficient dnns.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Dynamic network surgery for efficient dnns

Reference 14

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source=pdf_text observed=2026-08-09T17:26:45.358495Z digest=sha256:153b9877214dc6585f07fe27b76fa7b3b16b8a79b22ab476004ebf3be5ec018e

Observation a46df798-23c3-4dbe-a153-7dfaf4fb688a · outbound

This paper cites The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models

Reference 15

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Observation 512b8b2f-354e-4c26-a8fa-a2515c64d997 · outbound

This paper cites Inducingandexploiting activation sparsity for fast inference on deep neural networks.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Inducingandexploiting activation sparsity for fast inference on deep neural networks

Reference 16

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 27b8152c-4a77-45e3-b045-0bb3b45f1987 · outbound

This paper cites How Well Do Sparse Imagenet Models Transfer?.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs How Well Do Sparse Imagenet Models Transfer?

Reference 17

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Observation 42e2224b-2cea-473e-a876-be2a81e5587a · outbound

This paper cites ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models

Reference 18

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Observation cc1b31df-e2ee-4a88-8638-f35eb0bb1323 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 19

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Observation 5e30a072-ef1a-403f-9ee3-e8db08a86d02 · outbound

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

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs A Simple and Effective Pruning Approach for Large Language Models

Reference 20

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Observation 2bb0768b-f1d0-41c6-ae2b-0377f58ecf50 · outbound

This paper cites Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 21

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Observation ba707875-c073-40e2-aaac-dc89602d1248 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs OPT: Open Pre-trained Transformer Language Models

Reference 22

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Observation ac0b0cdf-f477-4b97-995a-1ad7497af0d7 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Adam: A Method for Stochastic Optimization

Reference 23

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source=pdf_text observed=2026-08-09T17:26:45.402602Z digest=sha256:28456e9f218a24f086411cd66b2a2f827e9c0d937350db3c340153c519d95978

Observation d42646ac-3cc1-470f-bbdd-b7bf07c4cb1c · outbound

This paper cites Robust principal component pursuit via inexact alternating minimization on matrix manifolds.Journal of Mathematical Imaging and Vision, 51(3):361–377, 2015.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Robust principal component pursuit via inexact alternating minimization on matrix manifolds.Journal of Mathematical Imaging and Vision, 51(3):361–377, 2015

Reference 24

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 4abf8e2f-a635-4420-a3c8-0a652f8fd3c2 · outbound

This paper cites Robust principal component analysis? Journal of the ACM (JACM), 58(3):1–37, 2011.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Robust principal component analysis? Journal of the ACM (JACM), 58(3):1–37, 2011

Reference 25

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raw_fallback, observed 2026-08-09T17:26:46.777223Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-09T17:26:45.413360Z digest=sha256:4881fb84fb91bd22814a2288c08f5f7ef069a4f30a0c4c73c5e457948d45e016

Observation ac5cbc91-60a2-45ad-9b45-b5922c8b8659 · outbound

This paper cites Linearized alternating direction method with adaptive penalty for low-rank representation.Advances in neural information processing systems, 24, 2011.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Linearized alternating direction method with adaptive penalty for low-rank representation.Advances in neural information processing systems, 24, 2011

Reference 26

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raw_fallback, observed 2026-08-09T17:26:46.759956Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-09T17:26:45.418338Z digest=sha256:74c4e57987051c99dd0f3106df42d5dcb9e7238518e9c277ad97faedd2b3d079

Observation 280fd5d6-1065-428f-8cd3-f2a111c8cf78 · outbound

This paper cites Parrilo, and Alan S.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Parrilo, and Alan S

Reference 27

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 201c6c66-04f5-4cd7-bde2-a1d533abfe47 · outbound

This paper cites Godec: Randomized low-rank & sparse matrix decomposition in noisy case.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Godec: Randomized low-rank & sparse matrix decomposition in noisy case

Reference 28

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raw_fallback, observed 2026-08-09T17:26:46.740989Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 287da164-a8b0-4b9a-8d51-aa2706cbd8af · outbound

This paper cites an unresolved cited work.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Unresolved cited work

Reference 29

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Observation 3ae5bc5a-6093-478f-8815-4bf347c40c60 · outbound

This paper cites Non-convex robust pca.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Non-convex robust pca

Reference 30

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raw_fallback, observed 2026-08-09T17:26:46.698004Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation eedea305-f1c7-4d65-b2c9-580925935b44 · outbound

This paper cites On compressing deep models by low rank and sparse decomposition.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs On compressing deep models by low rank and sparse decomposition

Reference 31

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raw_fallback, observed 2026-08-09T17:26:46.681679Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 554ab21c-97b7-49fe-80f7-fcef56e26e90 · outbound

This paper cites Losparse: Structured compression of large language models based on low-rank and sparse approximation.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Losparse: Structured compression of large language models based on low-rank and sparse approximation

Reference 32

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raw_fallback, observed 2026-08-09T17:26:46.664764Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 7394dcd8-34d1-4850-b1f0-7e4ff57ec7dd · outbound

This paper cites OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition

Reference 33

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source=pdf_text observed=2026-08-09T17:26:45.459024Z digest=sha256:48c0b58d875132513a6c5af7726374d8076ed08b896279ae23836f389b65f316

Observation 7657d52f-6d28-451b-b329-5051d37c9f0c · outbound

This paper cites SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs

Reference 34

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source=pdf_text observed=2026-08-09T17:26:45.465953Z digest=sha256:b5eb58d2c7175a64ebfe4337f8494e8ad74cef37d8becabbb3bfbae7e5c27401

Observation 1ebf4436-8f39-4af8-9da0-cbe9f8ab0b39 · outbound

This paper cites Springer, 2020.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Springer, 2020

Reference 35

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-09T17:26:45.472660Z digest=sha256:27eee0f63d708e7772ef1607df67a6e7f37cd501053877be2ea89b84e54b296a

Observation c7257759-b6f9-401d-9f4f-720fa22eab29 · outbound

This paper cites LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models

Reference 36

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source=pdf_text observed=2026-08-09T17:26:45.478477Z digest=sha256:b0961ed693fbb36d61544bc21f8dde317fcdb92f6211d031d3404e09d904fe6e

Observation 0f26366d-42d3-401e-b344-a8ac4a24df25 · outbound

This paper cites LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning

Reference 37

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source=pdf_text observed=2026-08-09T17:26:45.483874Z digest=sha256:2e10bf255e5c40df0fdf186d22046940bd0c9a2e51300be1eb2dcf9ba82d5dc2

Observation 08a1fa59-5d4d-44fe-8ef6-ee1d8cbb5c65 · outbound

This paper cites The approximation of one matrix by another of lower rank.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs The approximation of one matrix by another of lower rank

Reference 38

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source=pdf_text observed=2026-08-09T17:26:45.491839Z digest=sha256:787038a17b190073b03a5c63cb63c2ea570cf0ca1af68939a930385966ef2762

Observation 086e4763-1b66-484c-b7a1-351648872fa1 · outbound

This paper cites Rank-sparsity incoherence for matrix decomposition.SIAM Journal on Optimization, 21(2):572–596, 2011.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Rank-sparsity incoherence for matrix decomposition.SIAM Journal on Optimization, 21(2):572–596, 2011

Reference 39

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raw_fallback, observed 2026-08-09T17:26:46.618487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-09T17:26:45.496424Z digest=sha256:a781a0681fce0281997de32925a5c49f465150064400c454976ffd43e2d5c8cc

Observation b4ba7c4a-4e7c-400e-9446-9f7d58d7a776 · outbound

This paper cites Distributed opti- mizationandstatisticallearningviathealternatingdirectionmethodofmultipliers.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Distributed opti- mizationandstatisticallearningviathealternatingdirectionmethodofmultipliers

Reference 40

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raw_fallback, observed 2026-08-09T17:26:46.600479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-09T17:26:45.500648Z digest=sha256:38d6235a51cd43d33f304648ff4e9bb6e05c4147f49c76fb6f34ef716987c845

Observation 6525c939-4197-4870-bf76-9c1189247719 · outbound

This paper cites OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization

Reference 41

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source=pdf_text observed=2026-08-09T17:26:45.505094Z digest=sha256:0e10bb94ca386c8d4420816b133cb53839134c50f58ccf695cb5f26858c1b238

Observation 45212dc1-b6c0-452f-bed8-795df1285910 · outbound

This paper cites Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions.SIAM review, 53 (2):217–288, 2011.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions.SIAM review, 53 (2):217–288, 2011

Reference 42

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-09T17:26:45.510523Z digest=sha256:ec5c154baa20619825a661f6e0442567944780dd05bd7e0036796c07fad0c6de

Observation f20e03e8-1d00-40d6-b8b0-cf7fafa5379c · outbound

This paper cites an unresolved cited work.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Unresolved cited work

Reference 43

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-09T17:26:45.514878Z digest=sha256:fad57b668a2ad229d79d8e44fdaf95e70aafe7f6834974970d7ad45a873b7653

Observation 0df58e60-92b2-456f-8965-37ece2215c54 · outbound

This paper cites URL https://huggingface.co/docs/transformers/ perplexity.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs URL https://huggingface.co/docs/transformers/ perplexity

Reference 44

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raw_fallback, observed 2026-08-09T17:26:46.517129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-09T17:26:45.519093Z digest=sha256:6bc4f43dabbb192762543a5280b31404ff1537224329f3a9eef726b13e0a6537

Observation e7901336-8624-46c0-9a03-e256a2248aad · outbound

This paper cites Pointer sentinel mixture models.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Pointer sentinel mixture models

Reference 45

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raw_fallback, observed 2026-08-09T17:26:46.496944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-09T17:26:45.523563Z digest=sha256:49847e953a7239a3e0710e2a899708e1e967d2cee59826a58ade9afe1637d5a2

Observation 55b2dc7d-9376-4675-9a1f-b175c76e5f8e · outbound

This paper cites The penn treebank: Annotating predicate argument structure.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs The penn treebank: Annotating predicate argument structure

Reference 46

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source=pdf_text observed=2026-08-09T17:26:45.528782Z digest=sha256:ed1afeb84d100ab91e0846a9c04ca3f969f181cae7904e88a5dba4b926058a1e

Observation 3b3d258b-f4dc-4ddd-b414-5a6aa5a707ba · outbound

This paper cites A framework for few-shot language model evaluation, 12 2023.URL https://zenodo.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs A framework for few-shot language model evaluation, 12 2023.URL https://zenodo

Reference 47

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source=pdf_text observed=2026-08-09T17:26:45.534313Z digest=sha256:0a5325427d89e9e56f6b26dae14359ba3fd710ee37b1036d8ed423f8a8e6a7fd

Observation 1a252bdf-1e43-47fb-91c4-4604af9baa79 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Piqa: Reasoning about physical commonsense in natural language

Reference 48

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source=pdf_text observed=2026-08-09T17:26:45.539068Z digest=sha256:4067db8c09c4228279ce48a9d1f706103e50b7b8e718a6eddab242174d7bb3e4

Observation b198d023-76bd-4419-8e87-3419347873d8 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 49

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source=pdf_text observed=2026-08-09T17:26:45.544156Z digest=sha256:d03c502d1fda9ae0794dca155b7962b68ae66821e137a3eefb603dadfde6007e

Observation 25ae0331-935d-4ff9-9738-497c3c1dae24 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 50

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source=pdf_text observed=2026-08-09T17:26:45.549263Z digest=sha256:03d31effa631f88e79cc1657851b9b8f7d3e15d60522fc78e7ffa4f1152c4433

Observation ca9c5f48-ebd7-48c2-baf7-45844f0811a9 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021

Reference 51

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source=pdf_text observed=2026-08-09T17:26:45.554128Z digest=sha256:66cf2cf35eefef3924f3a109ee6491dd72722cd477ba748b406c78aec9a369b3

Observation 538f3029-6a30-48f6-a295-3e6ec5eb0112 · outbound

This paper cites A Survey on Recognizing Textual Entailment as an NLP Evaluation.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs A Survey on Recognizing Textual Entailment as an NLP Evaluation

Reference 52

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source=pdf_text observed=2026-08-09T17:26:45.559281Z digest=sha256:8d266d669de7e7c911198fb1a65516d127d0c50127e27035f67c2cbd8b37fac5

Observation dbdf7098-7262-45bf-98e9-7d8556f0eaae · outbound

This paper cites Careful Selection of Knowledge to solve Open Book Question Answering.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Careful Selection of Knowledge to solve Open Book Question Answering

Reference 53

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no resolver link, observed 2026-08-09T17:26:45.564609Z

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source=pdf_text observed=2026-08-09T17:26:45.564609Z digest=sha256:d69ff40986c778898b2b41ad6c554aa767e569a185ea913a897c6216def48ee6

Observation 6a160651-dce4-446a-b70c-ea8db0a525b3 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 54

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source=pdf_text observed=2026-08-09T17:26:45.569664Z digest=sha256:f8428bc02622de47976780cec7f3fe1c9e5cd7e818ef913f0b62f43627c7117d

Observation a96c3741-45ad-4b8d-932e-8e8febb790d8 · outbound

This paper cites Interactive supercomputing on 40,000 cores for machine learning and data analysis.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Interactive supercomputing on 40,000 cores for machine learning and data analysis

Reference 55

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verified exact
raw_fallback, observed 2026-08-09T17:26:45.686144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-09T17:26:45.574894Z digest=sha256:84fab390cef4bfcfa7893cbd75a16a6acb02fc0154a02371438b4a14c5658401

Pith citing papers

Observation 9cdb1949-aa79-4e33-abc6-f56763c1fa82 · inbound

ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity cites this paper.

ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs

Reference 5

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arxiv_id, observed 2026-05-11T23:26:12.815998Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-07T17:07:18.278784Z digest=sha256:394f5a2a57300ad9f3bdf098ca0b8748a3b9af25319fdb89ea9e615299c72bd4

Observation 5abc7b6e-40e9-4834-af70-f56c5fda748b · inbound

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression cites this paper.

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs

Reference 25

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arxiv_id, observed 2026-07-02T01:46:26.861562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T11:31:25.851340Z digest=sha256:492c81afeb84de0ca4f9c5d83cb96f9f2eadea5749bcb2e772e890861c3eeecf