Paper Citation Record · LEDGER

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices

As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.11093.

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

pith.paper-citation-record.v1
2506.11093 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:18:08.829114Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

31 of 31 outbound references displayed

verified exact2
verified fuzzy19
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fbf554ee-5ad0-4aa6-a90e-4ecd5a7aedb4 · outbound

This paper cites Quantization-aware policy distillation (qpd).

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Quantization-aware policy distillation (qpd)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.159637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 105abdbf-7510-4142-9daf-fd3206018389 · outbound

This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.738624Z

Source-reported events for the cited work

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Observation 62244e33-5b1c-451a-ae10-23c99305b597 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.741948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75305df3-d678-4345-adc2-3a05f6f548a0 · outbound

This paper cites Low-bit quantization of neural networks for efficient infer- ence.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Low-bit quantization of neural networks for efficient infer- ence

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.150386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 65a9c7ad-47e7-4fd4-8eec-42d18e04d16e · outbound

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

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Imagenet: A large-scale hierarchical image database

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.140604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad717c42-a3b7-4314-b3f4-748be58882a7 · outbound

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

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.752067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d744f39-8d72-453b-8314-e27ef9417f29 · outbound

This paper cites A survey of quan- tization methods for efficient neural network inference.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices A survey of quan- tization methods for efficient neural network inference

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.756084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6953aa4-29fe-4600-8776-fa97b94c099a · outbound

This paper cites HPTQ: Hardware-Friendly Post Training Quantization.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices HPTQ: Hardware-Friendly Post Training Quantization

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:18:08.921437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f1f980a-cd11-4df8-b53f-fab7b2b4458d · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Unetr: Transformers for 3d medical image segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.124761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 896868bd-56aa-40b9-86a0-5f87073d2733 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.114429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 07dd96a2-e38d-4736-8112-513380ccc293 · outbound

This paper cites Learning to quantize deep networks by optimizing quantization intervals with task loss.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Learning to quantize deep networks by optimizing quantization intervals with task loss

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.105147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad56fdb3-9551-478d-a9ed-18dd6cf95f04 · outbound

This paper cites Hyq: Hardware- friendly post-training quantization for cnn-transformer hybrid networks.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Hyq: Hardware- friendly post-training quantization for cnn-transformer hybrid networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.095731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8453153c-1d73-414a-96a8-586756b8b7b5 · outbound

This paper cites Im- agenet classification with deep convolutional neural networks.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Im- agenet classification with deep convolutional neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.086539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b69abdc-733e-4b35-a9ab-f720c9620b1f · outbound

This paper cites Q-hyvit: Post-training quantization of hybrid vision transformers with bridge block reconstruction for iot systems.IEEE Internet of Things Journal, 2024.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Q-hyvit: Post-training quantization of hybrid vision transformers with bridge block reconstruction for iot systems.IEEE Internet of Things Journal, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.076644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 38f74cb6-740b-4386-ac78-58367af3f543 · outbound

This paper cites Quantization for Rapid Deployment of Deep Neural Networks.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Quantization for Rapid Deployment of Deep Neural Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.780701Z

Source-reported events for the cited work

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Observation 5efc35ac-57fc-450c-b3cd-024287c81ad5 · outbound

This paper cites Efficient- former: Vision transformers at mobilenet speed.Advances in Neural Information Processing Systems, 35:12934–12949,.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Efficient- former: Vision transformers at mobilenet speed.Advances in Neural Information Processing Systems, 35:12934–12949,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.066315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 03a27a49-6617-43c1-8b0e-e0a7e5674871 · outbound

This paper cites Re- thinking vision transformers for mobilenet size and speed.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Re- thinking vision transformers for mobilenet size and speed

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.056582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6df56f6c-fb88-454a-b2cf-67fd06aac0e2 · outbound

This paper cites Bevformer: Learn- ing bird’s-eye-view representation from multi-camera images via spatiotemporal transformers.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Bevformer: Learn- ing bird’s-eye-view representation from multi-camera images via spatiotemporal transformers

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.045746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bbfb5b08-dc6b-49e1-9908-8da4f31ff824 · outbound

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

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Repq- vit: Scale reparameterization for post-training quantization of vision transformers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.035333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b6f8113f-9905-4e47-8d1f-cdcff30f04c5 · outbound

This paper cites Edgenext: efficiently amalga- mated cnn-transformer architecture for mobile vision appli- cations.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Edgenext: efficiently amalga- mated cnn-transformer architecture for mobile vision appli- cations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.025312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9444dc90-fb77-4df1-b8f2-c49c4a310e02 · outbound

This paper cites MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.798845Z

Source-reported events for the cited work

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Observation c0269a27-8f9e-45f0-a9ba-5511f777d021 · outbound

This paper cites Separable Self-attention for Mobile Vision Transformers.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Separable Self-attention for Mobile Vision Transformers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.801986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ab5abc5-e396-4304-9855-c9cc2ccadc02 · outbound

This paper cites Intriguing properties of vision transform- ers.Advances in Neural Information Processing Systems, 34: 23296–23308, 2021.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Intriguing properties of vision transform- ers.Advances in Neural Information Processing Systems, 34: 23296–23308, 2021

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.804858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a099aeae-b481-4e9b-b9d1-a9bbc27f6f97 · outbound

This paper cites An approximate memory architecture for en- ergy saving in deep learning applications.IEEE Transactions on Circuits and Systems I: Regular Papers, 67(5):1588–1601,.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices An approximate memory architecture for en- ergy saving in deep learning applications.IEEE Transactions on Circuits and Systems I: Regular Papers, 67(5):1588–1601,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:09.008167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 507729ba-efea-470f-bf94-489262478ef8 · outbound

This paper cites TensorRT.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices TensorRT

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:08.997898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c0ba478d-55e7-44b6-9d0e-9fd7e8ef1c1a · outbound

This paper cites Vision transformers on the edge: A comprehensive survey of model compression and acceleration strategies.Neurocomputing, page 130417, 2025.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Vision transformers on the edge: A comprehensive survey of model compression and acceleration strategies.Neurocomputing, page 130417, 2025

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:08.987735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2ec857b7-a076-4fcb-8ccf-d2df2f291f40 · outbound

This paper cites Pytorch image models.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Pytorch image models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:08.978052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 98666497-e498-47f2-8594-7bfb1d3340e5 · outbound

This paper cites EasyQuant: Post-training Quantization via Scale Optimization.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices EasyQuant: Post-training Quantization via Scale Optimization

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.819329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bb64f68-834d-402f-b653-49cb588eb6ac · outbound

This paper cites AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:18:08.863610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24cdad79-dcdf-42d4-a6a6-b37ab5771336 · outbound

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

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:08.825779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b4353dd-bc4d-46ee-b227-cb0b7f399485 · outbound

This paper cites Improving neural network quantization without retraining using outlier channel splitting.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Improving neural network quantization without retraining using outlier channel splitting

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:08.961160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.