Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Accelerating Transformer Inference and Training with 2:4 Activation Sparsity
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2503.16672.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:24:16.428845Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T19:43:54.723741Z
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 6ac0db3a-5086-4245-be3f-038de41f42d0 · inbound
TorchAO: PyTorch-Native Training-to-Serving Model Optimization cites this paper.
TorchAO: PyTorch-Native Training-to-Serving Model Optimization Accelerating Transformer Inference and Training with 2:4 Activation Sparsity
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:24:16.428845Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 78e5d969-af4f-4489-a24d-382810b76b13 · 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 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
Unavailable: canonical work link unavailable.
Observation e9e0fbe7-daf1-421f-8581-24cabc5e817f · 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 Accelerating Transformer Inference and Training with 2:4 Activation Sparsity
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T19:43:54.725241Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.