Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.13516.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T05:11:11.650290Z
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 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 2026-08-06T06:34:29.942622+00:00.