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Paper Citation Record · LEDGER

The Grammar-Learning Trajectories of Neural Language Models

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2109.06096.

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

pith.paper-citation-record.v1
2109.06096 v3

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 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T08:02:23.002090Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f439314e-0ee0-43ca-b6f9-40fe7000e41a · inbound

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling cites this paper.

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling The Grammar-Learning Trajectories of Neural Language Models

Reference 145

Resolution
verified exact
arxiv_id, observed 2026-05-15T17:45:17.736566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-15T17:45:17.540282Z digest=sha256:83a9376dd084e3cf0649cc0f8d5585f7927df3ff0b5d353a4317796232ce2834

Observation d71856ad-13aa-44db-8777-fd211d37cf3e · inbound

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model cites this paper.

Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model The Grammar-Learning Trajectories of Neural Language Models

Reference 217

Resolution
verified exact
arxiv_id, observed 2026-05-19T08:02:23.487671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-19T08:02:23.002090Z digest=sha256:a4e7d4482348c4f4f9bd969a00e5cd870c04c3972033b9758baa1cf04a6a854f