Paper Citation Record · LEDGER

8-bit Numerical Formats for Deep Neural Networks

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2206.02915.

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

pith.paper-citation-record.v1
2206.02915 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:36:11.660412Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T03:47:35.998768Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 79604980-7fa3-4640-a9a5-879902ba2fb0 · inbound

FP8 Formats for Deep Learning cites this paper.

FP8 Formats for Deep Learning 8-bit Numerical Formats for Deep Neural Networks

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T09:47:03.813968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2941aa7a-dfdf-4573-954c-7bdd74319b41 · inbound

An Inquiry into Datacenter TCO for LLM Inference with FP8 cites this paper.

An Inquiry into Datacenter TCO for LLM Inference with FP8 8-bit Numerical Formats for Deep Neural Networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T16:47:23.595301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c341977-195c-4989-8e1a-7882fbac0a1e · inbound

On Stochastic Rounding with Few Random Bits cites this paper.

On Stochastic Rounding with Few Random Bits 8-bit Numerical Formats for Deep Neural Networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T05:36:11.660412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ca4516e-5ff4-4660-80f3-c2b12c7be34b · 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 8-bit Numerical Formats for Deep Neural Networks

Reference 112

Resolution: unresolved; no resolver link, observed 2026-08-16T04:30:56.402776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9912a28d-4cdc-4678-b799-f8e45c294809 · inbound

DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models cites this paper.

DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models 8-bit Numerical Formats for Deep Neural Networks

Reference 124

Resolution: unresolved; no resolver link, observed 2026-08-06T17:47:40.326247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ef54b01-a8f3-417a-9951-9496e28b20a6 · inbound

Compute Requirements for Algorithmic Innovation in Frontier AI Models cites this paper.

Compute Requirements for Algorithmic Innovation in Frontier AI Models 8-bit Numerical Formats for Deep Neural Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T17:52:50.520117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93c4ca0f-0f0f-492e-9f4b-9839f0659c4e · 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 8-bit Numerical Formats for Deep Neural Networks

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:02:31.639481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b50e98f9-0593-4786-a61c-60667fda3273 · inbound

StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models cites this paper.

StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models 8-bit Numerical Formats for Deep Neural Networks

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T12:15:22.243362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7029b50d-e42f-464b-8b32-58eba4ec16e4 · inbound

Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning cites this paper.

Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning 8-bit Numerical Formats for Deep Neural Networks

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:56:16.427737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08da097b-1502-4d73-b2b5-f660d8c4b364 · inbound

An 83-Format Numeric Catalog with Bit-Exact Conformance Vectors: A Vendor-Neutral Reference for FP8, BF16, MXFP4, and Microscaling Formats cites this paper.

An 83-Format Numeric Catalog with Bit-Exact Conformance Vectors: A Vendor-Neutral Reference for FP8, BF16, MXFP4, and Microscaling Formats 8-bit Numerical Formats for Deep Neural Networks

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T03:47:36.000161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3424be5e-032d-435d-a309-a2ae40253e98 · inbound

Hadamard-Domain Model Quantization for Learned Image Coding cites this paper.

Hadamard-Domain Model Quantization for Learned Image Coding 8-bit Numerical Formats for Deep Neural Networks

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T23:36:18.802917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.