Pith. sign in

Paper Citation Record · LEDGER

Pruning Large Language Models with Semi-Structural Adaptive Sparse Training

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.20584.

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

pith.paper-citation-record.v1
2407.20584 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:27:13.850398Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:16:24.349651Z

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 9dc66177-e511-448d-826b-3c2e1ee6ba98 · inbound

Pruning General Large Language Models into Customized Expert Models cites this paper.

Pruning General Large Language Models into Customized Expert Models Pruning Large Language Models with Semi-Structural Adaptive Sparse Training

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T11:27:13.850398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:27:13.850398Z digest=sha256:27c89bdec5f60a2c7032e005da48d266ecc07a5ec6663c3acb1656ff26ec812c

Observation beeb33f7-77e1-4298-a91a-79e4c1d01dec · inbound

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity cites this paper.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Pruning Large Language Models with Semi-Structural Adaptive Sparse Training

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:16:24.436227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:16:20.505689Z digest=sha256:44a3eac6221ef3bb654606d0d8316b2e123f1ecc3d279a17d25b7b1462605d9a