Paper Citation Record · LEDGER

ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.13516.

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

pith.paper-citation-record.v1
2402.13516 v7

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:19:29.166818Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:39:45.462415Z

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 e48bf37d-762c-42aa-9a6f-26e6ffb3ccc8 · inbound

Instruction-Following Pruning for Large Language Models cites this paper.

Instruction-Following Pruning for Large Language Models ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T22:19:29.166818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63e38274-17ff-406f-8156-4598bbca26bd · inbound

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing cites this paper.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T21:19:23.992694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Unavailable: canonical work link unavailable.

Observation cef471c3-126f-4176-aaba-357b86743118 · inbound

GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation cites this paper.

GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:39:45.464016Z

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.