Paper Citation Record · LEDGER

Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.11458.

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

pith.paper-citation-record.v1
2502.11458 v1

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 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T05:04:00.410716Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T10:02:31.633000Z

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 2afeead0-f78f-4c56-b46a-2fd1adf104bb · inbound

Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention cites this paper.

Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:02:31.635398Z

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.

Observation b21c4f28-f0d4-4f4a-8356-708f18f180e1 · inbound

HiFloat4 Format for Language Model Pre-training on Ascend NPUs cites this paper.

HiFloat4 Format for Language Model Pre-training on Ascend NPUs Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:05:59.813944Z

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.

Observation 45068382-2ade-4d8b-b0a5-4f95c18e57bf · inbound

Pretraining large language models with MXFP4 on Native FP4 Hardware cites this paper.

Pretraining large language models with MXFP4 on Native FP4 Hardware Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:41:26.359156Z

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.

Observation dd58f1a8-55ee-483d-ab89-158c718f129b · inbound

Pretraining large language models with MXFP4 on Native FP4 Hardware cites this paper.

Pretraining large language models with MXFP4 on Native FP4 Hardware Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:28.700340Z

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.

Observation e20a6046-2332-4d6d-90c2-5a75fd2d929a · inbound

Pretraining large language models with MXFP4 on Native FP4 Hardware cites this paper.

Pretraining large language models with MXFP4 on Native FP4 Hardware Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:19:46.452970Z

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.

Observation 1c56a2d8-8645-42e7-b3a4-ba66f9822115 · inbound

Stable FP4 Training via Transposition-Invariant Block Quantization cites this paper.

Stable FP4 Training via Transposition-Invariant Block Quantization Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-31T05:04:00.410716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.