Paper Citation Record · LEDGER

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training

As of 16 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.11170.

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

pith.paper-citation-record.v1
2505.11170 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:03:33.493241Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact2
verified fuzzy6
unresolved31
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b24e17d4-2469-4faa-8d8a-ad8e2f6227b8 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.448608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efdd9ad8-b5b6-4235-bb41-1a2f43501eee · outbound

This paper cites an unresolved cited work.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Unresolved cited work

Reference 2

Resolution: malformed identifier; no resolver link, observed 2026-08-15T21:03:32.497412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b751dc2e-5131-4adf-886b-87038ba35c1d · outbound

This paper cites an unresolved cited work.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:03:35.489525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e67f9951-c2c3-4e79-9777-74fb7ab18f3d · outbound

This paper cites DeepSeek-V3 Technical Report.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training DeepSeek-V3 Technical Report

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.545119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6642a4b-277f-4368-b61d-a03158543476 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training QLoRA: Efficient Finetuning of Quantized LLMs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.625086Z

Source-reported events for the cited work

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Observation a6cb5fac-382e-4848-a08a-d79d49983cce · outbound

This paper cites Differentiable Model Compression via Pseudo Quantization Noise.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Differentiable Model Compression via Pseudo Quantization Noise

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.720775Z

Source-reported events for the cited work

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Observation b5a8f183-e0f0-4b36-9ee2-dea5f2df68ef · outbound

This paper cites Fishman, B.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Fishman, B

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:35.430815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 96f18cd5-8f46-47f8-81df-f316f4d8a023 · outbound

This paper cites Gokaslan and V.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Gokaslan and V

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.764394Z

Source-reported events for the cited work

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Observation b4e070fd-686e-400f-b9d1-a0231fd220cb · outbound

This paper cites Scaling FP8 training to trillion-token LLMs.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Scaling FP8 training to trillion-token LLMs

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.759252Z

Source-reported events for the cited work

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Observation 475da16d-741f-44d7-bd3f-2534cc503b89 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training LoRA: Low-Rank Adaptation of Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.778287Z

Source-reported events for the cited work

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Observation c74e4fb3-3e5e-4835-8e5c-2c535a1bdc85 · outbound

This paper cites Grattafiori, A.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Grattafiori, A

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:35.329749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 051c004b-2ccf-4e4c-af6b-4ad66df82042 · outbound

This paper cites Scaling Laws for Precision.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Scaling Laws for Precision

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.870353Z

Source-reported events for the cited work

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Observation b3ab9dc9-bc95-4e52-83a1-d7300cf14dd3 · outbound

This paper cites Lathrop, J.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Lathrop, J

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.923169Z

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Observation 498874f9-5005-436d-ade0-7916f8db51ea · outbound

This paper cites Karpathy.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Karpathy

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:35.316157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c0254d16-b418-4411-8460-b304a842bd8c · outbound

This paper cites LoQT: Low-Rank Adapters for Quantized Pretraining.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training LoQT: Low-Rank Adapters for Quantized Pretraining

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.932775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40acfe93-ad51-4f19-aab1-5886b936fe7b · outbound

This paper cites Decoupled Weight Decay Regularization.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Decoupled Weight Decay Regularization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.938904Z

Source-reported events for the cited work

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Observation fb0eb90c-e313-4ecf-bad4-93465fc17fde · outbound

This paper cites Liang, T.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Liang, T

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:35.067582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 46da2d5c-9e2a-4843-bd5a-8a850c644903 · outbound

This paper cites FP8 Formats for Deep Learning.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training FP8 Formats for Deep Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.947823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daa154e5-fb81-4f98-adf0-dc08598de893 · outbound

This paper cites an unresolved cited work.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:03:34.912299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 04a38b16-0343-44d7-a8a8-20864c665624 · outbound

This paper cites Mattson, A.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Mattson, A

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.943560Z

Source-reported events for the cited work

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Observation 7e510fbb-8dc6-4296-bec9-20dc97dec309 · outbound

This paper cites Radford, J.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Radford, J

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:34.750563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a7887ba7-5f70-4b29-85a6-4891398e6f57 · outbound

This paper cites Raffel, N.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Raffel, N

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:03:34.676654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d392c9fd-3866-4b21-b058-c4e2327980a3 · outbound

This paper cites ZeRO: Memory Optimizations Toward Training Trillion Parameter Models.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.102803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a2ddcd4-7abc-43ac-9641-174024e2ac93 · outbound

This paper cites an unresolved cited work.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:03:34.898709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2d8a81f2-f62a-4cdc-9149-5ab07bcd3cdd · outbound

This paper cites NIPQ: Noise proxy-based Integrated Pseudo-Quantization.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training NIPQ: Noise proxy-based Integrated Pseudo-Quantization

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:03:33.994963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a328083-1a4d-40a0-99f4-531ce6dba2c4 · outbound

This paper cites an unresolved cited work.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:03:34.626071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1040825b-70c1-4342-bff6-30b5523c2e0d · outbound

This paper cites Gemma 3 Technical Report.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Gemma 3 Technical Report

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.249658Z

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Observation 8c1aae26-53e6-46e7-bbda-440a8b63b9cb · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.254826Z

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Unavailable: canonical work link unavailable.

Observation 6bd37c6d-4928-4491-9011-c5ec9b5cdc82 · outbound

This paper cites Microscaling Data Formats for Deep Learning.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Microscaling Data Formats for Deep Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.107145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b32f834b-542c-4b27-8e52-d70ffbd5326a · outbound

This paper cites Attention Is All You Need.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Attention Is All You Need

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.338767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0b2eef1-50e3-45d4-a0e5-0c56a848d4bc · outbound

This paper cites Optimizing Large Language Model Training Using FP4 Quantization.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Optimizing Large Language Model Training Using FP4 Quantization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.476691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 326247b9-0773-43ae-b5e7-a8169aef1314 · outbound

This paper cites Scaling Laws for Floating Point Quantization Training.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Scaling Laws for Floating Point Quantization Training

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.244517Z

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Unavailable: canonical work link unavailable.

Observation a6b4c4d9-9ced-4b46-b4ea-99a9be05a8f7 · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.487723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b25dd6bd-35ff-4847-b463-a31567e7f612 · outbound

This paper cites APOLLO: SGD-like Memory, AdamW-level Performance.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training APOLLO: SGD-like Memory, AdamW-level Performance

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.493241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d4fff73-0d8f-464e-b762-f002f07774a4 · outbound

This paper cites Training LLMs with MXFP4.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Training LLMs with MXFP4

Reference 35

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

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Unavailable: canonical work link unavailable.

Observation b004f69f-60a0-41de-8ca3-a1f626edfa77 · outbound

This paper cites Adam-mini: Use Fewer Learning Rates To Gain More.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Adam-mini: Use Fewer Learning Rates To Gain More

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.482517Z

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Unavailable: canonical work link unavailable.

Observation 99a696e4-6776-45ce-b811-8c4c21b2b760 · outbound

This paper cites Romu: Fast Nonlinear Pseudo-Random Number Generators Providing High Quality.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Romu: Fast Nonlinear Pseudo-Random Number Generators Providing High Quality

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:03:34.103736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bb6d3808-fbf6-4c1d-acc2-5901fa13b8f7 · outbound

This paper cites FP8-LM: Training FP8 Large Language Models.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training FP8-LM: Training FP8 Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:33.088734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b285202-1292-4c21-9694-017a9f502d07 · outbound

This paper cites The Llama 3 Herd of Models.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training The Llama 3 Herd of Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.773330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites Oscillation-Reduced MXFP4 Training for Vision Transformers.

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

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.