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Paper Citation Record · LEDGER

Towards a theory of how the structure of language is acquired by deep neural networks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.00048.

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

pith.paper-citation-record.v1
2406.00048 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T18:32:14.480461Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T05:39:40.954589Z

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 6ccf4f19-16bc-483b-a777-17cc33e5f30a · inbound

(How) Can Transformers Predict Pseudo-Random Numbers? cites this paper.

(How) Can Transformers Predict Pseudo-Random Numbers? Towards a theory of how the structure of language is acquired by deep neural networks

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T18:32:14.480461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T18:32:14.480461Z digest=sha256:6acd185e8371065e267897123ee75cd772051b24f7e26e3477ee0db585df3f80

Observation 0d7c17cf-d775-4c77-a7c7-7221b72ebb51 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Towards a theory of how the structure of language is acquired by deep neural networks

Reference 215

Resolution
verified exact
arxiv_id, observed 2026-07-04T05:39:40.957208Z

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=2026-06-26T15:35:51.654392Z digest=sha256:e9c8f5f8d1b5df61affa4cb77098b2c16850f458dfb81bed0d51ff9e428f4ed3

Observation 5effa750-2c55-4d12-b8d9-f632aefde5b5 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Towards a theory of how the structure of language is acquired by deep neural networks

Reference 215

Resolution
verified exact
arxiv_id, observed 2026-07-02T21:57:25.567612Z

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=2026-07-02T21:51:13.457071Z digest=sha256:0796547f2e83e2dae4d9c50c346a432d6a3651f1f89a3e58ea69438f37b3f9d8