Paper Citation Record · LEDGER

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models

As of 14 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 2 inbound Pith citation observations for arXiv:2508.02128.

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

pith.paper-citation-record.v1
2508.02128 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:11:11.706409Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T19:40:42.033793Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T19:43:54.702984Z

Reference resolution

52 of 52 outbound references displayed

verified exact3
verified fuzzy1
unresolved48
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6cf075a2-4ae9-42b8-9f4e-7aa3b8ce7c01 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models , " * write output.state after.block = add.period write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T05:10:19.153810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02d4495d-3ed8-42f4-841d-ea9a3004499d · outbound

This paper cites write newline.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models write newline

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:10.995985Z

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Observation 17f7246f-6056-46fb-8ba4-7f746eb906ec · outbound

This paper cites LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.011704Z

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Observation 94b7c277-5380-4e0a-8e2f-29a8e9072ae9 · outbound

This paper cites L.; Gao, J.; and Choi, Y.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models L.; Gao, J.; and Choi, Y

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:11:12.394989Z

Source-reported events for the cited work

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Observation d22fda09-5907-4d84-8b35-8435bf4048c4 · outbound

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

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.147206Z

Source-reported events for the cited work

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Observation e0d8fa5f-545a-4ddd-a499-e02ec634198f · outbound

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

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.222099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b951e0b-a25e-4363-a852-0d24a4aeb331 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Training Verifiers to Solve Math Word Problems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.293076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceda6952-9fef-47d2-993d-2a9f3c0085fa · outbound

This paper cites Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.426493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b457c3f9-2abc-4177-81d1-555cf75d7bf9 · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.500179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaa00f33-b502-47b9-be16-0702e325d5ce · outbound

This paper cites an unresolved cited work.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-06T05:11:12.383184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 523da929-dc69-4c5a-a267-792382ca1c2a · outbound

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

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.510462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 146c1388-7cbc-4238-adbc-2f9f7feb242a · outbound

This paper cites an unresolved cited work.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.515162Z

Source-reported events for the cited work

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Observation 1478865e-500d-43a6-9930-000d799c9224 · outbound

This paper cites The Llama 3 Herd of Models.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models The Llama 3 Herd of Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.520813Z

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Observation 428b471c-e54d-40c7-bb57-69bac7a49e01 · outbound

This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models The Unreasonable Ineffectiveness of the Deeper Layers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.524583Z

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Unavailable: canonical work link unavailable.

Observation 58a9cd1f-f14c-4961-8596-33921d7bcd6d · outbound

This paper cites an unresolved cited work.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-06T05:11:12.363775Z

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Observation 553d8925-7d8a-4586-a599-fd05558c6ecb · outbound

This paper cites G.; and Wolff, G.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models G.; and Wolff, G

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.539165Z

Source-reported events for the cited work

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Observation 78e5d969-af4f-4489-a24d-382810b76b13 · outbound

This paper cites Accelerating Transformer Inference and Training with 2:4 Activation Sparsity.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Accelerating Transformer Inference and Training with 2:4 Activation Sparsity

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.542793Z

Source-reported events for the cited work

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Observation 41e5d15d-812d-4fd9-92b1-a4882a56973f · outbound

This paper cites an unresolved cited work.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-06T05:11:12.338397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 437085c9-cba2-4efc-b961-a88467b033b6 · outbound

This paper cites Accelerating Transformer Pre-training with 2:4 Sparsity.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Accelerating Transformer Pre-training with 2:4 Sparsity

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.551392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 638394ea-e553-4992-984f-ebbfa37df50e · outbound

This paper cites S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.555199Z

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Unavailable: canonical work link unavailable.

Observation cf2f87d0-69fc-4258-b3d6-353efe663b34 · outbound

This paper cites an unresolved cited work.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-06T05:11:12.324874Z

Source-reported events for the cited work

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Observation 714064f6-28d7-4856-a294-da645a826660 · outbound

This paper cites C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.565436Z

Source-reported events for the cited work

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Observation d65ff20b-b622-4cfa-be88-2d8371f85c71 · outbound

This paper cites SDQ: Sparse Decomposed Quantization for LLM Inference.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models SDQ: Sparse Decomposed Quantization for LLM Inference

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:11:12.094990Z

Source-reported events for the cited work

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Observation 6157d236-73a5-4c0e-8828-2c7859393a4d · outbound

This paper cites Semantic Retention and Extreme Compression in LLMs: Can We Have Both?.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Semantic Retention and Extreme Compression in LLMs: Can We Have Both?

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:11:12.076276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 857a773c-4cfb-4069-b06e-5f69e239ab81 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models CMMLU: Measuring massive multitask language understanding in Chinese

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.576316Z

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Observation 009f3c21-158a-422f-9d8b-0ea7d05a7886 · outbound

This paper cites E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.579620Z

Source-reported events for the cited work

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Observation 6388bbb7-dceb-4a14-9ec8-611602308aa8 · outbound

This paper cites The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.595425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4445f581-03eb-4892-a940-594eccb46c8e · outbound

This paper cites SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.599541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6783b33-6e59-4be3-8b78-907de6900b1c · outbound

This paper cites DeepSeek-V3 Technical Report.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models DeepSeek-V3 Technical Report

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.604627Z

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

This paper cites ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs.

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

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Observation d2bee87a-4886-4f4a-9731-77cef304fd5d · outbound

This paper cites Training-Free Activation Sparsity in Large Language Models.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Training-Free Activation Sparsity in Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.612043Z

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Observation 6132b3e9-6441-4e00-96ca-c0229775df50 · outbound

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Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Unresolved cited work

Reference 32

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Observation a147d658-17b5-4e36-a758-48fb36746110 · outbound

This paper cites Dynamic Activation Pitfalls in LLaMA Models: An Empirical Study.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Dynamic Activation Pitfalls in LLaMA Models: An Empirical Study

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:11:11.975564Z

Source-reported events for the cited work

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Observation 8a9528ad-6715-4758-be84-bbe6fa421587 · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 34

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Observation 7b39e5be-f133-454c-8fe0-503d604e97eb · outbound

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Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Unresolved cited work

Reference 35

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Observation bff4c606-33e3-4f43-8258-bc8ccc9cd123 · outbound

This paper cites ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Reference 36

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Source-reported events for the cited work

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Observation 58187fdc-6144-4711-8372-67817960edb3 · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Accelerating Sparse Deep Neural Networks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.642729Z

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Observation dd92140d-b171-44ce-8c9d-0eb824125b5c · outbound

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

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs

Reference 38

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Source-reported events for the cited work

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Observation 9f3f8849-7fbc-4139-a566-a8e02e33c21a · outbound

This paper cites ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.650290Z

Source-reported events for the cited work

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Observation 8cd80fb8-f662-4335-b9b1-3b3e636e1bb1 · outbound

This paper cites Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.654254Z

Source-reported events for the cited work

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Observation 69fb98c4-1057-417c-a0fe-6cf340a82895 · outbound

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

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models A Simple and Effective Pruning Approach for Large Language Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.657689Z

Source-reported events for the cited work

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Observation 81fc2f42-d6fc-47ee-a1d4-fe7949f56de9 · outbound

This paper cites Qwen2.5 Technical Report.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Qwen2.5 Technical Report

Reference 42

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Source-reported events for the cited work

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Observation f38ab2ba-39de-47f2-8465-208aee8fec83 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 43

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Source-reported events for the cited work

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Observation d50ee72f-5857-4cc9-a87f-a04627505cc0 · outbound

This paper cites Q-Sparse: All Large Language Models can be Fully Sparsely-Activated.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Q-Sparse: All Large Language Models can be Fully Sparsely-Activated

Reference 44

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Source-reported events for the cited work

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Observation df70ea74-766c-445c-9155-18b38a8cfb14 · outbound

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Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Unresolved cited work

Reference 45

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Source-reported events for the cited work

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Observation 29bb3fc5-a47b-46dc-8cdf-ad3fcbbaa66a · outbound

This paper cites Qwen3 Technical Report.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Qwen3 Technical Report

Reference 46

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Observation 5b210f18-6502-4ac0-82dd-6dcb8119751f · outbound

This paper cites GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference

Reference 47

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Observation c4466d57-885c-4e17-b2e8-adf3d8d70b50 · outbound

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

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition

Reference 48

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Source-reported events for the cited work

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Observation f1fa5052-8aea-446a-8b79-fc94fb105c97 · outbound

This paper cites FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.688996Z

Source-reported events for the cited work

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Observation 8a5aad82-a7e2-47bc-8c2b-4632188e5680 · outbound

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

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.693310Z

Source-reported events for the cited work

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Observation 916898f9-f702-4652-a5bc-2c8086d1dd4d · outbound

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Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-06T05:11:12.264204Z

Source-reported events for the cited work

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Observation 5c20c156-e7f4-4329-816b-56e55fc1328a · outbound

This paper cites Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.706409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e816b420-7e09-4425-98d7-ba16d22ca3e6 · inbound

Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches cites this paper.

Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:41:25.922214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f9e08ccf-b2b7-4364-8716-02e6955b4c0e · 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 Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:54.704636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.