Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.10912.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T06:00:51.812211Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T10:26:24.706388Z
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 ea833ff8-10f9-4fb1-90b2-905f69a19ac9 · inbound
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias cites this paper.
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T06:00:51.812211Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bd0d52fe-8eb6-4fb0-8b65-76e695a353d5 · inbound
Dynamic Sparse Training of Diagonally Sparse Networks cites this paper.
Dynamic Sparse Training of Diagonally Sparse Networks AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:15:15.173321Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation faa66efb-bc89-48e6-b6a9-b11e97515fa3 · inbound
SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks cites this paper.
SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T09:39:37.354724Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1e87c76d-2f96-4b48-a16d-4167011f5afb · inbound
A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs cites this paper.
A Replicate-and-Quantize Strategy for Plug-and-Play Load Balancing of Sparse Mixture-of-Experts LLMs AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T21:32:46.101296Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 608fe6d0-2316-4a45-a739-9e61fecb7bdb · inbound
Omega-S: A Functional Resilience Index for LLM Fine-Tuning cites this paper.
Omega-S: A Functional Resilience Index for LLM Fine-Tuning AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T10:26:24.710725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.