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Paper Citation Record · LEDGER

RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2204.12322.

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

pith.paper-citation-record.v1
2204.12322 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 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:52:24.509294Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:59:58.423984Z

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 66748328-28c7-4e29-8f0c-bbb6cfeba6b2 · inbound

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction cites this paper.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-16T04:52:24.509294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:52:24.509294Z digest=sha256:5f189e48b12756f9563956195549be469856e6da0e6a30054973b5d41b3f2781

Observation 61ebcf4c-af2e-4d1c-92d5-df9b3a7b7c17 · inbound

Neural Network Quantization by Learning Low-Loss Subspaces cites this paper.

Neural Network Quantization by Learning Low-Loss Subspaces RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-07-04T16:59:58.426063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-26T00:05:03.762579Z digest=sha256:9ec5a237a7fcefe3bec40981269af316a06cdac0ec2193d3f5ab3f2c52755570