Paper Citation Record · LEDGER

P$^2$U: Progressive Precision Update For Efficient Model Distribution

As of 20 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2506.22871.

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

pith.paper-citation-record.v1
2506.22871 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:03:18.111060Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

42 of 42 outbound references displayed

verified exact1
verified fuzzy37
unresolved4
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 04a616a8-6d52-4142-8c50-afafb392a6cf · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Very deep convolutional networks for large-scale image recognition,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:24.611334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bb5c0be2-6568-4244-a6bf-32aea7bb3bea · outbound

This paper cites Sparse: Sparse architecture search for cnns on resource-constrained microcontrollers,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Sparse: Sparse architecture search for cnns on resource-constrained microcontrollers,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:24.433624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c636f74a-721d-4a78-860e-8401833e675b · outbound

This paper cites Sparse binary compression: Towards distributed deep learning with minimal communication,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Sparse binary compression: Towards distributed deep learning with minimal communication,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:24.265833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dc804802-b2af-406f-9ba3-f27b46065152 · outbound

This paper cites Gradient sparsification for communication-efficient distributed optimization,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Gradient sparsification for communication-efficient distributed optimization,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:24.099734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4f61dca2-ae0f-4ebb-84d4-b34079d97c17 · outbound

This paper cites Compressing cnns using multilevel filter pruning for the edge nodes of multimedia internet of things,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Compressing cnns using multilevel filter pruning for the edge nodes of multimedia internet of things,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:23.966788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ac69abb1-1cc7-4ed0-951f-33ec65e1e673 · outbound

This paper cites An efficient pruning scheme of deep neural networks for internet of things applications,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution An efficient pruning scheme of deep neural networks for internet of things applications,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:23.777688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 30a4f950-644a-4238-b46e-f18c6109799d · outbound

This paper cites Stochastic binary-ternary quantization for communication efficient federated computation,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Stochastic binary-ternary quantization for communication efficient federated computation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:23.586977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2699b72e-7e11-49c1-af46-01298655eee3 · outbound

This paper cites Fixed-sign binary neural network: An efficient design of neural network for internet-of-things devices,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Fixed-sign binary neural network: An efficient design of neural network for internet-of-things devices,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:23.355991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cf1992c2-7483-472a-9c40-306d6b496523 · outbound

This paper cites Communication-efficient federated learning with binary neural networks,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Communication-efficient federated learning with binary neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:23.140268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 28fea155-6cdc-4667-9cd0-c4998d711a25 · outbound

This paper cites Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:22.876161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 38d4da6b-74eb-4dfe-aaf0-cc0fd2fc8520 · outbound

This paper cites Deepcabac: A universal compression algorithm for deep neural networks,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Deepcabac: A universal compression algorithm for deep neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:22.657197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ffe6ce26-a3eb-4183-8881-17aae610ff31 · outbound

This paper cites A White Paper on Neural Network Quantization.

P$^2$U: Progressive Precision Update For Efficient Model Distribution A White Paper on Neural Network Quantization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T22:03:15.702691Z

Source-reported events for the cited work

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Observation 16fca7fb-2f7d-4fc2-b897-ec3131f88421 · outbound

This paper cites The quantization model of neural scaling,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution The quantization model of neural scaling,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:22.469540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 879797a9-057f-40c0-b6f9-b77efe4e61af · outbound

This paper cites Adabits: Neural network quantization with adaptive bit-widths,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Adabits: Neural network quantization with adaptive bit-widths,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:22.289023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 25879251-d111-48fc-86f6-9c4d8f55248b · outbound

This paper cites Towards accurate post-training network quantization via bit-split and stitching,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Towards accurate post-training network quantization via bit-split and stitching,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:22.084099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f84b717e-4cc2-487b-8215-1c6a46cc21bd · outbound

This paper cites Progressive Weight Pruning of Deep Neural Networks using ADMM.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Progressive Weight Pruning of Deep Neural Networks using ADMM

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:03:18.326684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b1ed4e87-cb29-42c7-ad84-da94ea22b8f3 · outbound

This paper cites Dynamic network surgery for efficient dnns,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Dynamic network surgery for efficient dnns,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:21.946092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e5d91b6e-d66c-48cf-84f5-0e3ed8919bb1 · outbound

This paper cites Woodfisher: Efficient second-order approximation for neural network compression,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Woodfisher: Efficient second-order approximation for neural network compression,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:21.741372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e18b6d3e-d35e-4bce-8a68-555923e1b1b0 · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Pruning neural networks without any data by iteratively conserving synaptic flow,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:21.600543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d774a38a-042c-472a-b9a7-a50ffb5e42ed · outbound

This paper cites Hybrid pruning and sparsification,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Hybrid pruning and sparsification,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:21.339134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 782fe53e-1b43-4299-b232-e230ed576691 · outbound

This paper cites Deep neural network compression by in-parallel pruning-quantization,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Deep neural network compression by in-parallel pruning-quantization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:21.087137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 519ba595-d48d-49c0-bdc4-ebaee0011008 · outbound

This paper cites Pruning by explaining: A novel criterion for deep neural network pruning,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Pruning by explaining: A novel criterion for deep neural network pruning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:20.782852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0782b3da-37e3-44b2-809b-a9f089e657cb · outbound

This paper cites Joint matrix decomposition for deep convolutional neural networks compression,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Joint matrix decomposition for deep convolutional neural networks compression,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:20.460730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3128e4cc-6f32-41e8-a1f5-1dc4b1ff9674 · outbound

This paper cites Deep convolutional neural network compression method: Tensor ring decomposition with variational bayesian approach,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Deep convolutional neural network compression method: Tensor ring decomposition with variational bayesian approach,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:20.275938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ccad3b08-2ac9-429c-8549-9635a707a95e · outbound

This paper cites Sparse low rank factorization for deep neural network compression,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Sparse low rank factorization for deep neural network compression,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:20.149742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 733a577d-9743-4d33-afa3-a55aefa64524 · outbound

This paper cites Overview of the neural network compression and representation (nnr) standard,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Overview of the neural network compression and representation (nnr) standard,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:20.024070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 07221c2e-5e1f-41ca-9e85-e379fd51ba17 · outbound

This paper cites Model compression via distillation and quantization,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Model compression via distillation and quantization,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:19.940638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0d2dded9-76b9-40f9-8541-f8d2f7e02e77 · outbound

This paper cites DistilBERT, a distilled version of bert: smaller, faster, cheaper and lighter.

P$^2$U: Progressive Precision Update For Efficient Model Distribution DistilBERT, a distilled version of bert: smaller, faster, cheaper and lighter

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:19.858688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7d85aec3-ab79-40f9-816f-fccf8296f7a8 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Communication-efficient learning of deep networks from decentralized data,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:19.757360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fbb919a9-bb94-4e98-8d2c-c6c915307c4c · outbound

This paper cites Wireless federated distillation for distributed edge learning with heterogeneous data,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Wireless federated distillation for distributed edge learning with heterogeneous data,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:19.651899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e759a4c9-42c5-416d-92b1-fd013e41f209 · outbound

This paper cites Mix2fld: Downlink federated learning after uplink federated distillation with two-way mixup,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Mix2fld: Downlink federated learning after uplink federated distillation with two-way mixup,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:19.568010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 68515896-72eb-45fc-b900-67c30fd7b10a · outbound

This paper cites On the importance of temporal dependencies of weight updates in communication efficient federated learning,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution On the importance of temporal dependencies of weight updates in communication efficient federated learning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:19.409155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9fb66ad8-b1c6-4f5a-aeab-5012f749edbc · outbound

This paper cites Neural network coding of difference updates for efficient distributed learning communication,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Neural network coding of difference updates for efficient distributed learning communication,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:19.314968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 485819e8-23ab-4d0c-bbe1-f799f96ed209 · outbound

This paper cites Quantized compressed sensing for communication-efficient federated learning,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Quantized compressed sensing for communication-efficient federated learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:19.222929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation df262457-dd35-487b-9b01-796d928fe7ff · outbound

This paper cites Distributed learning of deep neural network over multiple agents,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Distributed learning of deep neural network over multiple agents,

Reference 35

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Source-reported events for the cited work

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Observation 5b474f8f-4e6f-4d08-8623-5e344599d5ab · outbound

This paper cites Traffic flow prediction with big data: A deep learning approach,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Traffic flow prediction with big data: A deep learning approach,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:19.083233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation adb03bb6-2797-46e0-9ada-4838a4071123 · outbound

This paper cites Large-scale transportation network congestion evolution prediction using deep learning theory,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Large-scale transportation network congestion evolution prediction using deep learning theory,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:18.935546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c19835bd-d86d-4c82-95cb-70a5e9614a81 · outbound

This paper cites Large dataset of labeled optical coherence tomography (OCT) and chest x-ray images,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Large dataset of labeled optical coherence tomography (OCT) and chest x-ray images,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:18.832478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eab43058-307b-4660-930b-4c1d9871808a · outbound

This paper cites The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T22:03:17.855053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 404ce8bf-bfae-4241-87bb-65a101d4efc9 · outbound

This paper cites Learning multiple layers of features from tiny images,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Learning multiple layers of features from tiny images,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T22:03:17.943079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4430aaa-dac5-4330-9ca1-63248d4236ed · outbound

This paper cites Nncodec: An open source software implementation of the neural network coding iso/iec standard,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Nncodec: An open source software implementation of the neural network coding iso/iec standard,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:18.658036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 41478be8-b5d8-4027-87f0-14128220e36b · outbound

This paper cites Overview of the neural network compression and representation (nnr) standard,.

P$^2$U: Progressive Precision Update For Efficient Model Distribution Overview of the neural network compression and representation (nnr) standard,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:03:18.469756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.