Paper Citation Record · LEDGER

Transformers Can Represent $n$-gram Language Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2404.14994.

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

pith.paper-citation-record.v1
2404.14994 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:33:21.763465Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:20:06.852432Z

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 06894e49-10b6-43be-86ba-55e10b525c71 · inbound

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning cites this paper.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Transformers Can Represent $n$-gram Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.950664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 157d7e55-b743-467d-9492-61bdb33c4707 · inbound

Learning curves theory for hierarchically compositional data with power-law distributed features cites this paper.

Learning curves theory for hierarchically compositional data with power-law distributed features Transformers Can Represent $n$-gram Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T22:33:21.763465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35763e32-4523-47cb-b442-5b0f433e6f36 · inbound

Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures cites this paper.

Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures Transformers Can Represent $n$-gram Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T22:31:44.339444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e83276c0-6e19-4f46-ae9a-f62c4c8e15db · inbound

Learning In-context n-grams with Transformers: Sub-n-grams Are Near-stationary Points cites this paper.

Learning In-context n-grams with Transformers: Sub-n-grams Are Near-stationary Points Transformers Can Represent $n$-gram Language Models

Reference 1948

Resolution: unresolved; no resolver link, observed 2026-08-15T17:26:36.907740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 042f6ecb-4c86-4589-8749-c7e67c912841 · inbound

Selective Induction Heads: How Transformers Select Causal Structures In Context cites this paper.

Selective Induction Heads: How Transformers Select Causal Structures In Context Transformers Can Represent $n$-gram Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T21:11:44.825896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f74f8b14-9228-48cf-a054-d9853d069f97 · inbound

Space-Efficient Language Generation in the Limit cites this paper.

Space-Efficient Language Generation in the Limit Transformers Can Represent $n$-gram Language Models

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:20:06.854207Z

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.