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Paper Citation Record · LEDGER

A Fast Post-Training Pruning Framework for Transformers

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2204.09656.

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

pith.paper-citation-record.v1
2204.09656 v2

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

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:46:38.283999Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T19:42:32.063057Z

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 b2ee4ca0-147a-4780-87d1-342092471b6d · inbound

Improving Dictionary Learning with Gated Sparse Autoencoders cites this paper.

Improving Dictionary Learning with Gated Sparse Autoencoders A Fast Post-Training Pruning Framework for Transformers

Reference 179

Resolution
verified exact
arxiv_id, observed 2026-05-17T19:42:32.065673Z

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.

source=arxiv_source observed=2026-05-17T19:42:31.855039Z digest=sha256:828b39de9df49078e51d96fd6c965cd01ba16fe7f2322fc2887cb6007868afd7

Observation b29ab30e-1ef3-43e1-8f81-b5f9563a56de · inbound

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

On Accelerating Edge AI: Optimizing Resource-Constrained Environments A Fast Post-Training Pruning Framework for Transformers

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-10T14:46:38.283999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T14:46:38.283999Z digest=sha256:484c3448257c19597bcb91e00192cc951b34ac8f775378bbce613817fc434e6f