Paper Citation Record · LEDGER

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs

As of 10 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2502.00258.

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

pith.paper-citation-record.v1
2502.00258 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:42:41.436411Z

measured 40 of 40 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 2026-08-06T05:11:11.608849Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T19:43:54.739687Z

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy4
unresolved33
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3f9964df-3505-42d3-892a-ae80afcac4bc · outbound

This paper cites GPT-4 Technical Report.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.320977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 253557ed-d3e7-4ed1-a25c-9d5abc623298 · outbound

This paper cites SparseLLM: Towards Global Pruning for Pre-trained Language Models.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs SparseLLM: Towards Global Pruning for Pre-trained Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.325205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df56ab46-4de8-44a0-8f8b-4acfaaa54c8e · outbound

This paper cites An alternating semiproximal method for nonconvex regularized structured total least squares problems.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs An alternating semiproximal method for nonconvex regularized structured total least squares problems

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:42:41.911100Z

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 d1713432-1100-4da2-aabc-013101b18181 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.332725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1b76240-4ede-4dc1-a59b-83659f64b14d · outbound

This paper cites Fast and Effective Weight Update for Pruned Large Language Models.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Fast and Effective Weight Update for Pruned Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.336372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccdbf154-5339-4e6d-b0b5-a65657cc3cad · outbound

This paper cites Learning to Compress Prompt in Natural Language Formats.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Learning to Compress Prompt in Natural Language Formats

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.340018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f575af71-2dfe-4dc6-a8d2-756ed6b24928 · outbound

This paper cites A dynamic alternating direction of multipliers for nonconvex minimization with nonlinear functional equality constraints.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs A dynamic alternating direction of multipliers for nonconvex minimization with nonlinear functional equality constraints

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:42:41.900793Z

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 3700b71c-0215-44a9-b085-19273f1a5b60 · outbound

This paper cites MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.346481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b597e834-ba66-4edb-8982-2db50856558a · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.350141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 658b4c75-0a12-4fa3-9ad3-459d0d96bf87 · outbound

This paper cites and Alistarh, D.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs and Alistarh, D

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.353297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c89fcf7d-9685-4d24-937c-d8037712e7fd · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.356415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f26e591d-0398-47d8-9ec9-f947f2d4bed8 · outbound

This paper cites A framework for few-shot language model evaluation, 07 2024.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs A framework for few-shot language model evaluation, 07 2024

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.359779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56f288a4-ef20-4630-a8b4-ff32327854e5 · outbound

This paper cites and Liu, H.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs and Liu, H

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:42:41.885648Z

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 01bda5a5-6cf9-4fed-a601-a30798fd6bfb · outbound

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

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.364914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49b2e97c-55b7-4db2-80f9-d92deb7ad163 · outbound

This paper cites Pruning Large Language Models with Semi-Structural Adaptive Sparse Training.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Pruning Large Language Models with Semi-Structural Adaptive Sparse Training

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.367419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cebb6e6a-e211-4a42-b8a9-a7f9ddba8145 · outbound

This paper cites Mistral 7B.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Mistral 7B

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.370326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aeb551ff-4283-4734-a769-abe0cf0bea30 · outbound

This paper cites A Proximal Operator for Inducing 2:4-Sparsity.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs A Proximal Operator for Inducing 2:4-Sparsity

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T19:42:41.571845Z

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 3ad03afe-0e68-4d58-8e6a-bacf4476db92 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.375936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22863de5-7164-4ea7-899e-17076d693d1b · outbound

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

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.378889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45009686-45be-44c0-9249-0908a0766ed3 · outbound

This paper cites W., and Yang, Y.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs W., and Yang, Y

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.382027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9cd3484-91b1-4af1-9f1b-3ae6a894aa5d · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Llm-pruner: On the structural pruning of large language models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.384613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57e62e8d-2069-453a-8747-ba3fc2001847 · outbound

This paper cites Pointer sentinel mixture models, 2016.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Pointer sentinel mixture models, 2016

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.387579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77302a96-31b4-4366-8dcc-7e2ae682391f · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Accelerating Sparse Deep Neural Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.390434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bae2b174-b52a-41c9-b199-41304c822df0 · outbound

This paper cites Gradient methods for minimizing composite functions.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Gradient methods for minimizing composite functions

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:42:41.854868Z

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 a559ffa1-4e47-4db3-840d-2e45b931d216 · outbound

This paper cites Stochastic Rounding for LLM Training: Theory and Practice.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Stochastic Rounding for LLM Training: Theory and Practice

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.395993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d350950-1f86-45e8-afd7-90f6f397e214 · outbound

This paper cites an unresolved cited work.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.399410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 232cb4d1-bd75-45bc-a3cc-83f037f3b931 · outbound

This paper cites an unresolved cited work.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-09T19:42:41.839029Z

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 f02067e2-a6cb-4fd7-af34-e2d03860a793 · outbound

This paper cites Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.404912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7038abe-4d9f-42b6-be13-7b5c08d2c740 · outbound

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

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs A Simple and Effective Pruning Approach for Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.408512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 316a731c-8346-4f01-8e9c-b7ca6d64b8d3 · outbound

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

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.411926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2135b4e7-81a8-42d8-8f41-5321d983ab34 · outbound

This paper cites Training LLMs with MXFP4.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Training LLMs with MXFP4

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.415287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a78d1c00-a2b7-4aa7-8d85-47157c92ea8b · outbound

This paper cites Emergent Abilities of Large Language Models.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Emergent Abilities of Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.418279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 273f1c42-f69e-4b2a-85de-c71a14c63435 · outbound

This paper cites RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.421127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77a8b4ed-8efe-4c36-ac5b-ee025772108e · outbound

This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.424152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9dd8a23-4299-48cb-917d-ba5dba53a982 · outbound

This paper cites Qwen2.5 Technical Report.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Qwen2.5 Technical Report

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.427558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed13a50e-67c2-4b94-b76c-a7dbe3d16b68 · outbound

This paper cites Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.430360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fee9afb-2407-4e3f-9155-b9a192ac264e · outbound

This paper cites KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.433628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e22cf1e7-2f02-4f57-bd4c-72bd889c4f7f · outbound

This paper cites write newline.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs write newline

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T19:42:41.436411Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0be974c8-6aeb-439d-91d4-c5c3dc03f558 · inbound

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models cites this paper.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.608849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc604385-c1a5-4de3-a336-b774be362aaf · inbound

RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models cites this paper.

RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:54.741286Z

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.