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Paper Citation Record · LEDGER

What Matters In The Structured Pruning of Generative Language Models?

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2302.03773.

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

pith.paper-citation-record.v1
2302.03773 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:21:23.570687Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T07:36:55.152927Z

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 f9bb6cca-ea5f-462f-b87e-a3b640c22764 · inbound

TinyStories: How Small Can Language Models Be and Still Speak Coherent English? cites this paper.

TinyStories: How Small Can Language Models Be and Still Speak Coherent English? What Matters In The Structured Pruning of Generative Language Models?

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-25T07:36:55.156637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-25T07:36:55.087443Z digest=sha256:0c809ceff40157133b9d6921a8b637df91e4ec8fd7d0211a6253791ae7dcce2a

Observation 44967318-9bc6-4813-a3a1-b68448dcf17e · inbound

On Accelerating Edge AI: Optimizing Resource-Constrained Environments cites this paper.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments What Matters In The Structured Pruning of Generative Language Models?

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-10T14:46:38.321953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T14:46:38.321953Z digest=sha256:837f5485a5c92c58a331c781b5de413a5b484aeac2401c55da5f115653c40664

Observation f61c9a06-f5c9-452c-b827-fb2832b42f46 · inbound

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models cites this paper.

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models What Matters In The Structured Pruning of Generative Language Models?

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-10T15:21:23.570687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T15:21:23.570687Z digest=sha256:4de10e5f00aec4dcf52e69612fa49701377d79b4f453762b0ae260ab169332d0

Observation 80007b7f-b52a-4a6f-8321-32203d1285be · inbound

Post-Training Neural Network Pruning using Graph Curvature cites this paper.

Post-Training Neural Network Pruning using Graph Curvature What Matters In The Structured Pruning of Generative Language Models?

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-03T08:45:16.488503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:45:16.488503Z digest=sha256:00fbedb7f10aec39e8ca3d04ef3ca34cb7e098d33997e0fd429346fb7a09e4cc