Paper Citation Record · LEDGER

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers

As of 15 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 3 inbound Pith citation observations for arXiv:2506.11784.

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

pith.paper-citation-record.v1
2506.11784 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:10:27.946858Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:00:11.211052Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T20:59:01.914274Z

Reference resolution

38 of 38 outbound references displayed

verified exact2
verified fuzzy11
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1e01dc83-aa24-409d-bee9-6ff53a874d9a · outbound

This paper cites Qwen Technical Report.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Qwen Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:24.443463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e934503f-a622-450a-a495-1cc7a2502053 · outbound

This paper cites Food-101 – mining discriminative components with random forests.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Food-101 – mining discriminative components with random forests

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:24.547304Z

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Observation b67651c2-1841-462b-8c03-8ec8baceb87e · outbound

This paper cites EfficientQAT: Efficient Quantization-Aware Training for Large Language Models.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:24.638411Z

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Observation cf3d4568-0304-42b5-a53c-c614e382edf0 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Imagenet: A large- scale hierarchical image database

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:24.730895Z

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Observation 8ea7e39b-c03e-4e6d-9660-81836a598189 · outbound

This paper cites Packqvit: Faster sub-8-bit vision transformers via full and packed quantization on the mobile.Advances in Neural Information Processing Systems, 36:9015–9028, 2023.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Packqvit: Faster sub-8-bit vision transformers via full and packed quantization on the mobile.Advances in Neural Information Processing Systems, 36:9015–9028, 2023

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:30.556998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 93c2f23f-c766-4f6f-ace3-e664e7f798ee · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:24.889101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 290b2395-8c77-4bbb-b477-f9a3d3714391 · outbound

This paper cites Learned Step Size Quantization.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Learned Step Size Quantization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:24.991159Z

Source-reported events for the cited work

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Observation 76055404-152e-4ce0-8320-2991a8124931 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:25.079101Z

Source-reported events for the cited work

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Observation 14fbf50e-08e3-4b08-a4f7-be4a85d06ead · outbound

This paper cites Quantization without Tears.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Quantization without Tears

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:10:28.442548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9ea60ff8-ae59-4610-bb25-04918ab3aa9b · outbound

This paper cites Dtl: Disentangled transfer learning for visual recognition.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Dtl: Disentangled transfer learning for visual recognition

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:30.542096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 03c3528c-532e-446a-ac9b-4869d9717e0f · outbound

This paper cites Knowledge distillation: A survey.International Journal of Computer Vision, 129(6):1789–1819, 2021.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Knowledge distillation: A survey.International Journal of Computer Vision, 129(6):1789–1819, 2021

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:25.316542Z

Source-reported events for the cited work

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Observation 15ac01c7-69bf-4e9c-9822-980b9b131cbd · outbound

This paper cites Mask r-cnn.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Mask r-cnn

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:30.501321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3a62fdb9-a4b7-4a34-b3d3-ee5c30d9f89a · outbound

This paper cites Deep residual learning for image recognition.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Deep residual learning for image recognition

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:25.503033Z

Source-reported events for the cited work

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Observation 677de6f1-f2f3-45a3-8d3e-0e40b71beb70 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Distilling the Knowledge in a Neural Network

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:25.601292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c6ee4ad-a407-4225-812d-968fa8ab3ed3 · outbound

This paper cites Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:25.688306Z

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Observation 758a6474-04ad-4f51-a4eb-0f64ac714f7e · outbound

This paper cites AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:10:28.257883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1018fb9c-b78d-4b03-96a7-d36bc650e44c · outbound

This paper cites 3d object representations for fine- grained categorization.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers 3d object representations for fine- grained categorization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:30.171050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b3d030bd-c23e-4408-9150-e320cc27b6d7 · outbound

This paper cites A comprehensive study on quantization techniques for large language models.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers A comprehensive study on quantization techniques for large language models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:29.841164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 21516da7-253f-4c25-9175-0e9295294033 · outbound

This paper cites Q-vit: Accurate and fully quantized low-bit vision transformer.Advances in neural information processing systems, 35:34451–34463, 2022.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Q-vit: Accurate and fully quantized low-bit vision transformer.Advances in neural information processing systems, 35:34451–34463, 2022

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:26.025033Z

Source-reported events for the cited work

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Observation 2aee966c-b454-4583-a3e0-157982fb7a0e · outbound

This paper cites Repq-vit: Scale reparameterization for post-training quantization of vision transformers.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Repq-vit: Scale reparameterization for post-training quantization of vision transformers

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:26.117770Z

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Observation 5935e76f-98d9-4936-ae08-8c73b0a9544b · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.Proceedings of Machine Learning and Systems, 6:87–100, 2024.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Awq: Activation-aware weight quantization for on-device llm compression and acceleration.Proceedings of Machine Learning and Systems, 6:87–100, 2024

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:26.200934Z

Source-reported events for the cited work

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Observation 06149630-cbb3-46c1-b4f0-d53567ea76fb · outbound

This paper cites Microsoft coco: Common objects in context.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Microsoft coco: Common objects in context

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:29.626936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5189078e-1837-4f7c-a2dd-d52b5e9bb291 · outbound

This paper cites Oscillation-free quantization for low-bit vision transformers.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Oscillation-free quantization for low-bit vision transformers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:26.340435Z

Source-reported events for the cited work

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Observation 27915e7c-a97c-40e5-9d5c-13bb0d1e3992 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Swin transformer: Hierarchical vision transformer using shifted windows

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:26.414069Z

Source-reported events for the cited work

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Observation b6355401-5bcc-47db-9cc9-48416c888d41 · outbound

This paper cites Post-training quantization for vision transformer.Advances in Neural Information Processing Systems, 34:28092–28103, 2021.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Post-training quantization for vision transformer.Advances in Neural Information Processing Systems, 34:28092–28103, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:29.433935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 73432717-44ec-4918-bb8b-66fade8ca003 · outbound

This paper cites Decoupled Weight Decay Regularization.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Decoupled Weight Decay Regularization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:26.576696Z

Source-reported events for the cited work

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Observation a3f4e855-2c42-495c-9a68-a5a569638c23 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Fine-Grained Visual Classification of Aircraft

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:26.682151Z

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Observation ee7dde31-e3f6-44a3-9dcd-044c4882f6f0 · outbound

This paper cites Automated flower classification over a large number of classes.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Automated flower classification over a large number of classes

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:26.781084Z

Source-reported events for the cited work

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Observation 4576f02a-78c7-4ae2-88f1-c74e2829e81f · outbound

This paper cites A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:29.277761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 884e492b-edfd-4ccc-9b70-bb98a5f6b39d · outbound

This paper cites Cats and dogs.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Cats and dogs

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:27.045069Z

Source-reported events for the cited work

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Observation ac6f942a-a28a-4227-98b7-45e22f3a989d · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Training data-efficient image transformers & distillation through attention

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:29.096046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0651f0f4-b191-4cc3-9681-ca3671b6e4e8 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers LLaMA: Open and Efficient Foundation Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:27.352627Z

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Observation 4c16e1e8-5414-4603-817b-1a7018ec1246 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:27.484033Z

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Unavailable: canonical work link unavailable.

Observation 0a08e7e0-1c0b-4566-a6fa-925e221706e1 · outbound

This paper cites Distilling knowledge by mimicking features.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):8183–8195, 2021.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Distilling knowledge by mimicking features.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):8183–8195, 2021

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:27.579787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ceecc11-e986-4671-b07a-2b1d8e01b491 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:27.673857Z

Source-reported events for the cited work

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Observation c4ff25ec-4453-4d00-b988-b90c66439944 · outbound

This paper cites Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T04:10:27.767390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85eb8003-5215-4382-aecf-f1abbb4e21c9 · outbound

This paper cites All you need in knowledge distillation is a tailored coordinate system.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers All you need in knowledge distillation is a tailored coordinate system

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:28.921010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation abd47f39-d7b4-4290-8459-eec0f34887a5 · outbound

This paper cites Quantized feature distillation for network quantization.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Quantized feature distillation for network quantization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:10:28.741344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation b3d5f5ca-bd7a-4f53-9fd6-fd422fe16497 · inbound

YOLOv8-SMOT: An Efficient and Robust Framework for Real-Time Small Object Tracking via Slice-Assisted Training and Adaptive Association cites this paper.

YOLOv8-SMOT: An Efficient and Robust Framework for Real-Time Small Object Tracking via Slice-Assisted Training and Adaptive Association GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:11.211052Z

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Unavailable: canonical work link unavailable.

Observation ab584475-4a57-468f-9cf3-6a01ce9920b8 · inbound

Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay cites this paper.

Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:56:07.909331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 224edf11-20ec-4a76-a565-02bdf8e46300 · inbound

Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization cites this paper.

Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:59:01.916802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.