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Paper Citation Record · LEDGER

Training LLMs with MXFP4

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2502.20586.

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

pith.paper-citation-record.v1
2502.20586 v3

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measured 0 of 0 reference resolution

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measured 22 of 22 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:03:33.260318Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:36:12.313960Z

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Outbound references

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Pith citing papers

Observation 2135b4e7-81a8-42d8-8f41-5321d983ab34 · inbound

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs cites this paper.

ProxSparse: Regularized Learning of Semi-Structured Sparsity Masks for Pretrained LLMs Training LLMs with MXFP4

Reference 31

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no resolver link, observed 2026-08-09T19:42:41.415287Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T19:42:41.415287Z digest=sha256:0b5d4e7d65bc12e254b150ceea64d1e6a60a100cc1f5b391256542c66b1ceb48

Observation 3d4fff73-0d8f-464e-b762-f002f07774a4 · 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 Training LLMs with MXFP4

Reference 35

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no resolver link, observed 2026-08-15T21:03:33.260318Z

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source=pdf_text observed=2026-08-15T21:03:33.260318Z digest=sha256:5adb4d6f539cdd2e0ba351283bb3def5dc2ba3571b822526e27f58e653987d45

Observation 9c62804d-be7c-4241-939c-aaa8d16d30ba · inbound

Scaling Law for Quantization-Aware Training cites this paper.

Scaling Law for Quantization-Aware Training Training LLMs with MXFP4

Reference 39

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source=pdf_text observed=2026-08-07T15:41:08.862934Z digest=sha256:defc717734675f8c86ba9a6e58c557f5b3c1d10f040e1bb0cc4f645b371021f6

Observation 30346e9c-fc01-46c8-b573-e6a49430264e · inbound

FP4 All the Way: Fully Quantized Training of LLMs cites this paper.

FP4 All the Way: Fully Quantized Training of LLMs Training LLMs with MXFP4

Reference 19

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no resolver link, observed 2026-08-07T14:25:39.162969Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:25:39.162969Z digest=sha256:926035a4cdac5d99f937bd57a9bfe769120d4e862ffb3738411fc2a51039cd70

Observation 2ad6dd0c-2c4d-450a-8a9c-19ee36aabc99 · inbound

Recipes for Pre-training LLMs with MXFP8 cites this paper.

Recipes for Pre-training LLMs with MXFP8 Training LLMs with MXFP4

Reference 30

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no resolver link, observed 2026-08-07T12:12:47.436064Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:12:47.436064Z digest=sha256:a5390ddc098cd1cae1bd42e2396e994b3f53e58f5421c2e37e75b23e6dde4f62

Observation ca415c67-07c8-4bb1-bf9b-d9649486e814 · inbound

OASIS: Outlier-Aware LUT-Based GEMM with Dual-Side Quantization for LLM Inference Acceleration cites this paper.

OASIS: Outlier-Aware LUT-Based GEMM with Dual-Side Quantization for LLM Inference Acceleration Training LLMs with MXFP4

Reference 54

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no resolver link, observed 2026-08-06T11:15:34.031488Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:15:34.031488Z digest=sha256:a9a96a865cce75dbcf93fb147da86c95b8af0d1a5203e7b4f122d416ce1315cc

Observation 0ee60707-4b4f-478a-ab91-f55567e38442 · 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 Training LLMs with MXFP4

Reference 28

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arxiv_id, observed 2026-05-18T10:02:31.760017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-18T10:01:56.131253Z digest=sha256:cce664d87beb3c203f0a4bf626abb9d89587f3f319369195b0b2bd9ec3b2d6c8

Observation 72a1e432-e10a-4265-b36e-1250f8ef0ad8 · inbound

Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling cites this paper.

Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling Training LLMs with MXFP4

Reference 8

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arxiv_id, observed 2026-05-17T02:23:52.645493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-17T02:23:01.845123Z digest=sha256:4b91da29afd8f8d1985e4a6a62fea025a0aed4f3eaf1ad5e9e4bf385f9e8f7c7

Observation 25d502e3-7e94-4bf5-8b95-3035822adc04 · inbound

What is New in Stochastic Rounding: a Survey on Theory, Hardware, and Applications cites this paper.

What is New in Stochastic Rounding: a Survey on Theory, Hardware, and Applications Training LLMs with MXFP4

Reference 35

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T14:02:44.140957Z digest=sha256:e3ac837da91c50012b20075514a6a16a93b6ce0287f2af59ddb5f71e08106ff0

Observation cbecd944-f006-4ae6-bb3b-6e4d659d0916 · inbound

VFA: Relieving Vector Operations in Flash Attention with Global Maximum Pre-computation cites this paper.

VFA: Relieving Vector Operations in Flash Attention with Global Maximum Pre-computation Training LLMs with MXFP4

Reference 24

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arxiv_id, observed 2026-05-11T09:16:00.023031Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T16:10:40.858525Z digest=sha256:955e5a93bccf87b5af4b343118dad6100bd6fabb412adbde561cf5b33dbbe76f

Observation 49da8b73-31d6-4119-89de-76020a426fa0 · inbound

Grid Games: The Power of Multiple Grids for Quantizing Large Language Models cites this paper.

Grid Games: The Power of Multiple Grids for Quantizing Large Language Models Training LLMs with MXFP4

Reference 38

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arxiv_id, observed 2026-05-13T06:47:26.520686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-13T06:44:59.501345Z digest=sha256:b1151d0ad71284d9fadbe64831017b04f3fd450d25829822cd038ed59541f6c1

Observation 5e6d2583-f5b3-4f09-a78f-298ec611c463 · inbound

Search Your Block Floating Point Scales! cites this paper.

Search Your Block Floating Point Scales! Training LLMs with MXFP4

Reference 158

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arxiv_id, observed 2026-05-13T06:02:24.073575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-13T05:52:26.558984Z digest=sha256:dde7aae6038293fa8a4de8bfc5524e1d4a9c2e4fbe10c97e11034f9881b6a80b

Observation 54b05a9c-2c78-4252-b9c0-9351b7eab367 · inbound

Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor cites this paper.

Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor Training LLMs with MXFP4

Reference 37

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arxiv_id, observed 2026-05-21T07:59:50.002964Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-21T07:59:43.755196Z digest=sha256:440362dbb3c09b04da64b648d2dde0a2bddd600b74b02531a56fc28f15dd4afd

Observation 18e29319-1425-4b09-8442-593447c4cf42 · inbound

Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor cites this paper.

Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor Training LLMs with MXFP4

Reference 37

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arxiv_id, observed 2026-05-25T05:50:23.721565Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-25T05:49:08.484663Z digest=sha256:e6caa15f0b16c7564a3c0f5d0a37ea2e213f67a6b8b474b61ce59b4d61365ebd

Observation 8551d3bc-abaf-4612-b817-f9aff05f8454 · inbound

Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor cites this paper.

Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor Training LLMs with MXFP4

Reference 37

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arxiv_id, observed 2026-06-30T18:04:57.989957Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-06-30T18:01:38.509794Z digest=sha256:9b1820805cdf1ee380c3204ca850d696edf5d4229ca316f9724681c8b017d433

Observation 352a5745-22c9-4eb6-845c-1f2c47c8fed3 · inbound

Stochastic Rounding Increases Small Singular Values cites this paper.

Stochastic Rounding Increases Small Singular Values Training LLMs with MXFP4

Reference 10

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arxiv_id, observed 2026-07-01T20:26:13.686138Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T21:02:34.955394Z digest=sha256:ed63ac3d40629623c3d63125c7cf817dbcd994752d069706434ebf500c4e6a91

Observation 13654279-a9b7-47bd-aa9b-a45b319cb38b · inbound

Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation cites this paper.

Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation Training LLMs with MXFP4

Reference 11

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arxiv_id, observed 2026-07-01T20:36:12.315547Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-28T18:15:19.879507Z digest=sha256:a6ab8632d218d30bc5fdb6dd3e9cccafd91fffc91e7fe5d17c8dc2095a15bf21

Observation 1738a3a2-3203-4f81-bbd8-8603df924636 · 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 Training LLMs with MXFP4

Reference 20

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source=pdf_text observed=2026-07-11T19:17:59.044982Z digest=sha256:dd8dbf291b1b2f4dc426e81e2221d47acaddfd258fdd3c9c1e2182f8f9236b7d

Observation db2aac41-95f8-4dab-9921-d875adc75179 · inbound

Reference Traces for Auditing Invisible Weight Updates and Guiding Exact-Budget Protection cites this paper.

Reference Traces for Auditing Invisible Weight Updates and Guiding Exact-Budget Protection Training LLMs with MXFP4

Reference 26

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source=pdf_text observed=2026-07-14T15:32:26.691504Z digest=sha256:3cd19923eca7a9f14b0ed9d556fb5fbfc0015561e2d40a53172747921bd2fd3d

Observation 7300d75f-2f49-4c8c-9904-e69dbccdb10b · inbound

CANN Bench: Benchmarking Agent Generated Kernels against Real NPU and Algorithmic Limits cites this paper.

CANN Bench: Benchmarking Agent Generated Kernels against Real NPU and Algorithmic Limits Training LLMs with MXFP4

Reference 17

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no resolver link, observed 2026-08-02T08:11:51.965627Z

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source=pdf_text observed=2026-08-02T08:11:51.965627Z digest=sha256:f6788bb8895ad3db91cd257ae7dcdc7c517cdb0c0b88da891ac41633ecef7ad7

Observation bbb63f90-a6d0-40e8-899d-8bc3351116a2 · inbound

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

Stable FP4 Training via Transposition-Invariant Block Quantization Training LLMs with MXFP4

Reference 17

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source=pdf_text observed=2026-07-31T05:04:00.386588Z digest=sha256:0f9fe544082d96c4ca3fa6e50f288519b89abdf80e21847a5cd01c8a140f021a

Observation f67e8c93-a6f1-4516-986c-ab1c8d5c4500 · inbound

HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models cites this paper.

HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models Training LLMs with MXFP4

Reference 50

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source=pdf_text observed=2026-08-01T14:11:20.981325Z digest=sha256:be24b12813ffb0d4d8b1c4d1e0c40f090a0a117774113d833275a231286c9867