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Paper Citation Record · LEDGER

N-Grammer: Augmenting Transformers with latent n-grams

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2207.06366.

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

pith.paper-citation-record.v1
2207.06366 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 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T06:58:22.362262Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

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 33bf2d83-ad89-4aea-9060-6bf8aa7bb2e3 · inbound

Decoupling the Benefits of Subword Tokenization for Language Model Training via Byte-level Simulation cites this paper.

Decoupling the Benefits of Subword Tokenization for Language Model Training via Byte-level Simulation N-Grammer: Augmenting Transformers with latent n-grams

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-09T04:10:08.740419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-07T10:05:24.009460Z digest=sha256:367966d00932ea67b19a57bc12d0103d47b2711bb53bc3a897fcbe64c95b7e4e

Observation 97eafd81-ed5e-4fe8-920b-a215c943f402 · inbound

Decoupling the Benefits of Subword Tokenization for Language Model Training via Byte-level Simulation cites this paper.

Decoupling the Benefits of Subword Tokenization for Language Model Training via Byte-level Simulation N-Grammer: Augmenting Transformers with latent n-grams

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:59:49.082208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-15T06:58:22.362262Z digest=sha256:7999403afb99a4c4ee9ad9f49f47329ea9b19915c8ebd0d014e39bf5a26a955b