Paper Citation Record · LEDGER

Accelerating Transformer Pre-training with 2:4 Sparsity

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2404.01847.

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

pith.paper-citation-record.v1
2404.01847 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:46:44.714941Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T23:24:26.177866Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0ceaeec2-1bed-4570-95c7-cc9478a32d68 · inbound

TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks cites this paper.

TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks Accelerating Transformer Pre-training with 2:4 Sparsity

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:44.714941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d152cb25-3ae8-4cb1-8d13-4394713d80a0 · inbound

Dynamic Sparse Training of Diagonally Sparse Networks cites this paper.

Dynamic Sparse Training of Diagonally Sparse Networks Accelerating Transformer Pre-training with 2:4 Sparsity

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:15:13.191537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e8ea87d-19f0-421a-8b69-89f8d73116b5 · inbound

From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction cites this paper.

From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction Accelerating Transformer Pre-training with 2:4 Sparsity

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:47:07.661120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 437085c9-cba2-4efc-b961-a88467b033b6 · 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 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 a12b4328-f815-4a3f-8ca5-13a6bc1c1354 · inbound

Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs cites this paper.

Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs Accelerating Transformer Pre-training with 2:4 Sparsity

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:24:26.181121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b512806d-1f0f-4c30-9fdb-84921885c9b3 · 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 Accelerating Transformer Pre-training with 2:4 Sparsity

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:41:25.953148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation edea3182-9e17-4034-85ef-fd05b472ab74 · 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 Accelerating Transformer Pre-training with 2:4 Sparsity

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:56:29.823108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.