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Paper Citation Record · LEDGER

nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2304.06875.

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

pith.paper-citation-record.v1
2304.06875 v4

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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:37:29.567091Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T21:05:09.611244Z

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 33ec028a-4950-4a2f-aceb-4dcaba5c7de4 · inbound

Training LLMs on HPC Systems: Best Practices from the OpenGPT-X Project cites this paper.

Training LLMs on HPC Systems: Best Practices from the OpenGPT-X Project nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-22T21:05:09.614699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-22T21:04:11.859563Z digest=sha256:8f5a47e9dfd476b479925cc622bddd2e31833b3996d6aacc7235ca569fb7fdc2

Observation 8672deed-b502-4933-8609-e00e170f9591 · inbound

RoboEgo System Card: An Omnimodal Model with Native Full Duplexity cites this paper.

RoboEgo System Card: An Omnimodal Model with Native Full Duplexity nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T11:37:29.567091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:37:29.567091Z digest=sha256:672bb0919bb96bc5cca05c2b0c2cd296bc0f30df805ab920f45dd89129ba2058