Paper Citation Record · LEDGER

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

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.15929.

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

pith.paper-citation-record.v1
2310.15929 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:55:16.237795Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:19:31.610899Z

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 cb41fb77-3e26-496c-a5ce-c5c50e307dd8 · inbound

Symmetric Pruning of Large Language Models cites this paper.

Symmetric Pruning of Large Language Models E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T21:55:16.237795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd6f7fc3-64e4-4249-a72e-1674e1ebc7e2 · inbound

RAP: Runtime Adaptive Pruning for LLM Inference cites this paper.

RAP: Runtime Adaptive Pruning for LLM Inference E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.633263Z

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

Unavailable: canonical work link unavailable.

Observation ad6439a2-c767-43ce-8328-dd892f56a284 · inbound

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity

Reference 135

Resolution: unresolved; no resolver link, observed 2026-08-04T21:06:26.312670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71ab4286-940a-4d4d-a6fe-7a2dd858a93c · 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 E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:54.749967Z

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 07690f09-398d-4388-ba78-6ed68f37ea05 · inbound

Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs cites this paper.

Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:19:31.612542Z

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.