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Paper Citation Record · LEDGER

Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2201.08442.

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

pith.paper-citation-record.v1
2201.08442 v1

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:43:16.690934Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T11:56:55.968899Z

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 5f2fbf29-3e30-40db-ad8d-6216628dffa4 · inbound

Low-Resolution Neural Networks cites this paper.

Low-Resolution Neural Networks Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T23:43:16.690934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T23:43:16.690934Z digest=sha256:56f36fcbc43ceaf3c8f3de7c4c778c9e600a9a97a91c0f34d92cd6b354a76af0

Observation 1a9e6798-db5b-48bc-8a61-37b43e3c291f · inbound

When Good Enough Is Optimal: Multiplication-Only Matrix Inversion Approximation for Quantized Gated DeltaNet cites this paper.

When Good Enough Is Optimal: Multiplication-Only Matrix Inversion Approximation for Quantized Gated DeltaNet Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-07-02T11:56:55.970243Z

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=2026-06-28T02:36:03.243732Z digest=sha256:fa6b1cdd39ceae9938fcc49efecc08cacb94748996a23b9a49b1ab6668611a00