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Paper Citation Record · LEDGER

SparseLLM: Towards Global Pruning for Pre-trained 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:2402.17946.

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

pith.paper-citation-record.v1
2402.17946 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:34:13.801474Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9d3130fa-c900-4122-8c3b-b58019f6b5e4 · inbound

$\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts cites this paper.

$\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts SparseLLM: Towards Global Pruning for Pre-trained Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T14:34:13.801474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:34:13.801474Z digest=sha256:fd58746f53f19d89d7784637293025b38a54858360ada0eb4dc1e83cc09afa27

Observation 87daeb5e-04ac-4e54-871e-f20e5ff89569 · inbound

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models cites this paper.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models SparseLLM: Towards Global Pruning for Pre-trained Language Models

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T21:08:18.637391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:08:18.637391Z digest=sha256:ead6e53e1cc228008ebb5fd325b52987a1aca4435c506333128779739aed081d

Observation eff92234-204d-4c08-b8d6-b094c36268cd · inbound

NeuroAgent: LLM Agents for Multimodal Neuroimaging Analysis and Research cites this paper.

NeuroAgent: LLM Agents for Multimodal Neuroimaging Analysis and Research SparseLLM: Towards Global Pruning for Pre-trained Language Models

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-08T22:14:19.962567Z

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.

source=arxiv_source observed=2026-05-08T09:40:28.520988Z digest=sha256:64375ede2c5430e519e037f281ac17d6f7985941d723c67496716e9d9a641b5b

Observation 8b0b8c0b-897d-4eaa-9599-020da4570fd7 · inbound

NeuroAgent: LLM Agents for Multimodal Neuroimaging Analysis and Research cites this paper.

NeuroAgent: LLM Agents for Multimodal Neuroimaging Analysis and Research SparseLLM: Towards Global Pruning for Pre-trained Language Models

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:16:11.166911Z

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.

source=arxiv_source observed=2026-05-08T09:40:28.520988Z digest=sha256:47864fefb736ed6835ab8a7c1399ef7576e39717edf9c260036053f4019ccb3e

Observation 884e62bf-1f11-4fdc-a963-096bf82432ee · inbound

EinSort: Sorting is All We Need for Tensorizing LLM cites this paper.

EinSort: Sorting is All We Need for Tensorizing LLM SparseLLM: Towards Global Pruning for Pre-trained Language Models

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-07-02T23:07:26.462625Z

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.

source=arxiv_source observed=2026-06-27T18:31:01.804061Z digest=sha256:d4175791d02a25f9a8ae0043217cecc4bbc7f6af3f04a8484040df965abc68a4