Paper Citation Record · LEDGER

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

As of 10 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 2026-08-10T06:31:04.303077+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
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.269580Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.276912Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.286036Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.291926Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.937761Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.305454Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.311804Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.907768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.327737Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.333519Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.878366Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.347753Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.353766Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.358495Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.832348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:26:46.283061Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.375949Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.381428Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.387344Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.392303Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.397279Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.402602Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.797287Z

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.777223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.759956Z

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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.740989Z

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

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.698004Z

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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.664764Z

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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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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.647251Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.478477Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.483874Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.491839Z

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.618487Z

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.600479Z

Source-reported events for the cited work

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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.579399Z

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

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

Reference 43

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.517129Z

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:26:46.496944Z

Source-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.528782Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.534313Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.539068Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.544156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.549263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.554128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.559281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.569664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: 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 2026-08-10T06:31:04.303077+00:00.

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

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:26:12.815998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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

Resolution: verified exact; 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 2026-08-10T06:31:04.303077+00:00.