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Paper Citation Record · LEDGER

Wanda++: Pruning Large Language Models via Regional Gradients

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.04992.

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

pith.paper-citation-record.v1
2503.04992 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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:46:07.703817Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:23:39.057729Z

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 dbb3e0a1-ccc6-4cff-8278-ae71b90eb5be · inbound

SepPrune: Structured Pruning for Efficient Deep Speech Separation cites this paper.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Wanda++: Pruning Large Language Models via Regional Gradients

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-15T20:46:07.703817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:46:07.703817Z digest=sha256:ce904fe9d94dac5ecda21eb7dd7966346426f338c351aa734326a8e5c5b2a241

Observation db0d1abe-a396-4129-9a3b-84e7c9344286 · inbound

ACE: Exploring Activation Cosine Similarity and Variance for Accurate and Calibration-Efficient LLM Pruning cites this paper.

ACE: Exploring Activation Cosine Similarity and Variance for Accurate and Calibration-Efficient LLM Pruning Wanda++: Pruning Large Language Models via Regional Gradients

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:23:39.155438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:23:34.229021Z digest=sha256:02f84729646108479d117e983c89aac7b6003ed61616cbf5cfe4195682ef78fa