Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.13199.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:59:21.945152Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T05:30:23.456663Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
pith, observed 2026-08-10T05:30:23.456663Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation cc56bfc5-33f8-41f4-908f-5b9832044e55 · inbound
SPAP: Structured Pruning via Alternating Optimization and Penalty Methods cites this paper.
SPAP: Structured Pruning via Alternating Optimization and Penalty Methods CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:59:21.945152Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3defe7bf-cf5e-49c7-8324-460f89e2e9a0 · inbound
EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models cites this paper.
EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information
Reference 58
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T12:09:11.412685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.