Paper Citation Record · LEDGER

Scaling Optimal LR Across Token Horizons

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2409.19913.

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

pith.paper-citation-record.v1
2409.19913 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:31:19.177588Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:08:57.588255Z

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 6a28f7e1-ff55-45c8-98e4-2c82ae95876f · inbound

MiniCPM4: Ultra-Efficient LLMs on End Devices cites this paper.

MiniCPM4: Ultra-Efficient LLMs on End Devices Scaling Optimal LR Across Token Horizons

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:31:19.177588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c446ab7-09a0-4d66-a75f-a58d7ed45590 · inbound

Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model cites this paper.

Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model Scaling Optimal LR Across Token Horizons

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:00:43.291720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f9f0d20b-6b44-40ad-9c62-9ff06ce409e8 · inbound

How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size cites this paper.

How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size Scaling Optimal LR Across Token Horizons

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:08:57.589992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation db4d0e64-30a4-4959-871d-4543df27f422 · inbound

Scale Weight Decay and Train Better cites this paper.

Scale Weight Decay and Train Better Scaling Optimal LR Across Token Horizons

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-30T12:53:41.048646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.