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Paper Citation Record · LEDGER

GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2412.17560.

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

pith.paper-citation-record.v1
2412.17560 v4

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:11:11.681322Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T19:36:32.906884Z

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 5b210f18-6502-4ac0-82dd-6dcb8119751f · inbound

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models cites this paper.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T05:11:11.681322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T05:11:11.681322Z digest=sha256:c27ab4d53883662218be2f52b6339517486defa547ee5be04f527b6d5fd3c1f9

Observation 4a1d5dc8-537f-4413-85b7-01a81edf5ab1 · inbound

S2O: Early Stopping for Sparse Attention via Online Permutation cites this paper.

S2O: Early Stopping for Sparse Attention via Online Permutation GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-15T19:36:32.911204Z

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=2026-05-15T19:32:52.948154Z digest=sha256:29f1d30ec3e62719456f02ffbf316b921d2088e4bde3ab879e72c003d5434d56