Paper Citation Record · LEDGER

Oscillation-Reduced MXFP4 Training for Vision Transformers

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.20853.

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

pith.paper-citation-record.v1
2502.20853 v2

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 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:30:56.548690Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T12:59:52.351777Z

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 e8d07ac6-4f9f-48e5-9536-deaf8ff64123 · inbound

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities cites this paper.

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities Oscillation-Reduced MXFP4 Training for Vision Transformers

Reference 143

Resolution: unresolved; no resolver link, observed 2026-08-16T04:30:56.548690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21271101-8f90-4a48-8a44-1dbeb2f4e247 · inbound

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training cites this paper.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Oscillation-Reduced MXFP4 Training for Vision Transformers

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.539790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6566d8b-818e-42c1-abf6-c72f4da43cae · inbound

Not All NVFP4 QAT Recipes Are Equal: How Architecture and Scale Shape Model Quality for Anomaly Segmentation cites this paper.

Not All NVFP4 QAT Recipes Are Equal: How Architecture and Scale Shape Model Quality for Anomaly Segmentation Oscillation-Reduced MXFP4 Training for Vision Transformers

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:13:48.342496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 141a0ea9-81f5-4427-934e-3f3e5bb95757 · inbound

SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference cites this paper.

SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference Oscillation-Reduced MXFP4 Training for Vision Transformers

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-04T12:59:52.353211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2f1d7d13-1cc6-4bf0-9599-bfa8ccffa5f2 · inbound

Full-Stack FP4: Stable LLM Pretraining with Quantized Projections, Optimizers, and Attention cites this paper.

Full-Stack FP4: Stable LLM Pretraining with Quantized Projections, Optimizers, and Attention Oscillation-Reduced MXFP4 Training for Vision Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-11T19:17:59.044982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a831031-1cc6-4c21-93be-60ef7c223387 · inbound

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

Stable FP4 Training via Transposition-Invariant Block Quantization Oscillation-Reduced MXFP4 Training for Vision Transformers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-31T05:04:00.321201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.