Paper Citation Record · LEDGER

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits

As of 9 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2509.00133.

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

pith.paper-citation-record.v1
2509.00133 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:11:15.527514Z

measured 15 of 15 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 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

15 of 15 outbound references displayed

verified exact1
verified fuzzy11
unresolved3
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 85b2ed80-a790-4188-aec2-8d2444fd9fb2 · outbound

This paper cites Gradient Flows: in Metric Spaces and in the Space of Probability Measures.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Gradient Flows: in Metric Spaces and in the Space of Probability Measures

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.694970Z

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 84025171-09ef-43e7-a6a7-ccbfd5856d4e · outbound

This paper cites Estimating or propagating gradients through stochastic neurons for conditional computation, 2013.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Estimating or propagating gradients through stochastic neurons for conditional computation, 2013

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.684999Z

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 37c0860e-1a63-4188-8c20-78c85807ad64 · outbound

This paper cites On lazy training in differentiable programming.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits On lazy training in differentiable programming

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.675237Z

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 a71001d8-0de2-418a-a7a9-9fcb5ec465a0 · outbound

This paper cites Binaryconnect: Training deep neural networks with binary weights during propagations.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Binaryconnect: Training deep neural networks with binary weights during propagations

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.664843Z

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 8ef084ec-47e2-441f-bbf8-e316064ea08e · outbound

This paper cites Binarized neural networks, 2016.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Binarized neural networks, 2016

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.654919Z

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 bdba1c5e-aef7-421a-94e0-e4d71cfe5e8a · outbound

This paper cites Ternary Weight Networks.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Ternary Weight Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:15.494158Z

Source-reported events for the cited work

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Observation a62c5522-a006-4b23-bd8a-e7004834da85 · outbound

This paper cites A mean-field analysis of deep resnet and beyond: Towards provable optimization via overparameterization from depth, 2020.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits A mean-field analysis of deep resnet and beyond: Towards provable optimization via overparameterization from depth, 2020

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.643592Z

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 04252a6e-4078-46f7-bc19-095c990c3417 · outbound

This paper cites Mean field limit of the learning dynamics of multilayer perceptrons, 2019.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Mean field limit of the learning dynamics of multilayer perceptrons, 2019

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.633904Z

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 9ff20144-e931-4c3f-a08e-2028bc3737d3 · outbound

This paper cites Xnor-net: Imagenet classification using binary convolutional neural networks.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Xnor-net: Imagenet classification using binary convolutional neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.623223Z

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 09eb4cc9-5f31-466d-adc4-cf8b518f3b6f · outbound

This paper cites Optimal transport for applied mathematicians.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Optimal transport for applied mathematicians

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.613267Z

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 5f179aa4-70ca-4bb1-880c-2a3af1f9fd92 · outbound

This paper cites Mean field analysis of neural networks: A central limit theorem.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Mean field analysis of neural networks: A central limit theorem

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.603001Z

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 175fea6a-4451-4bc5-8904-8290ced42696 · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:15.516591Z

Source-reported events for the cited work

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Observation 38b0a9a1-82a1-4778-aa71-25bd18451261 · outbound

This paper cites Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:11:15.558459Z

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 c9cc72ba-6430-4fc5-aed1-7ee6323b67f1 · outbound

This paper cites Understanding straight-through estimator in training activation quantized neural nets.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Understanding straight-through estimator in training activation quantized neural nets

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:15.524426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1356b782-1148-4e4e-97bd-fa07a381f241 · outbound

This paper cites Lq-nets: Learned quantization for highly accurate and compact deep neural networks.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Lq-nets: Learned quantization for highly accurate and compact deep neural networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:11:15.587771Z

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.

Pith citing papers

No inbound Pith citation observations are available.