Paper Citation Record · LEDGER

AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.10912.

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

pith.paper-citation-record.v1
2410.10912 v1

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 2026-08-11T06:34:44.6726+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:40:41.586478Z

measured 0 of 1 external citation measurements

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 70c6fafe-75d3-49f8-add8-3a0de9d099d9 · inbound

Adaptive Pruning for Large Language Models with Structural Importance Awareness cites this paper.

Adaptive Pruning for Large Language Models with Structural Importance Awareness AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T11:40:41.586478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-11T06:34:44.6726+00:00.