Paper Citation Record · LEDGER

QwT-v2: Practical, Effective and Efficient Post-Training Quantization

As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 2 inbound Pith citation observations for arXiv:2505.20932.

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

pith.paper-citation-record.v1
2505.20932 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:48:30.736528Z

measured 47 of 47 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:00:11.926420Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T20:59:01.882823Z

Reference resolution

45 of 45 outbound references displayed

verified exact0
verified fuzzy39
unresolved6
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bdc3d67b-d6fb-4654-b35d-7d9a42be4973 · outbound

This paper cites write newline.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization write newline

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95fa9f49-3803-45c8-9f9b-064a10c6edcb · outbound

This paper cites BERT : Pre-training of deep bidirectional transformers for language understanding.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization BERT : Pre-training of deep bidirectional transformers for language understanding

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:39.460637Z

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Observation 533560de-9d54-4351-91f3-48ecbc8e1923 · outbound

This paper cites an unresolved cited work.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:48:39.164343Z

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Observation a96d340c-dd55-43a3-9bdb-9fc153c47c93 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization An image is worth 16x16 words: Transformers for image recognition at scale

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:38.855065Z

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Observation fb28f136-aa4c-49c9-a302-fb38ab977eaf · outbound

This paper cites Mask R-CNN.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Mask R-CNN

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:38.633343Z

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Observation 48c1c222-d66a-4240-b85f-3ce654aa17d2 · outbound

This paper cites S peech GPT : Empowering large language models with intrinsic cross-modal conversational abilities".

QwT-v2: Practical, Effective and Efficient Post-Training Quantization S peech GPT : Empowering large language models with intrinsic cross-modal conversational abilities"

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:38.393437Z

Source-reported events for the cited work

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Observation 8fc4ca32-4cff-4168-9edc-ecdd4f1e9595 · outbound

This paper cites Learning transferable visual models from natural language supervision.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Learning transferable visual models from natural language supervision

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:38.108535Z

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Observation e20ce6cc-076c-4eb6-a672-2dcbb989f692 · outbound

This paper cites Visual instruction tuning.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Visual instruction tuning

Reference 8

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Observation 1cccac59-85d2-44cd-8e0e-d629ac562add · outbound

This paper cites Pruning and quantization for deep neural network acceleration: A survey.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Pruning and quantization for deep neural network acceleration: A survey

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:37.622358Z

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Observation f182b4e1-0724-47d6-8111-d1d903a734c8 · outbound

This paper cites BRECQ : Pushing the limit of post-training quantization by block reconstruction.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization BRECQ : Pushing the limit of post-training quantization by block reconstruction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:37.428648Z

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Observation a1e5fa4c-5902-4852-8b84-aa43a0e2a663 · outbound

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

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Smoothquant: accurate and efficient post-training quantization for large language models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:37.211136Z

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Observation a2bc934d-b893-4364-870c-29ba8d044c3b · outbound

This paper cites Learned step size quantization.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Learned step size quantization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:37.041203Z

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Observation aee14e9f-67ca-4176-9fa9-9e7caa4647aa · outbound

This paper cites Quantized feature distillation for network quantization.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Quantized feature distillation for network quantization

Reference 13

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Source-reported events for the cited work

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Observation 43e2e41f-e694-4e35-84ce-397a659c12df · outbound

This paper cites Q-ViT : Accurate and fully quantized low-bit Vision Transformer.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Q-ViT : Accurate and fully quantized low-bit Vision Transformer

Reference 14

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Observation 30d08ca3-248d-42a7-99db-3250d404afb1 · outbound

This paper cites Quantization without tears.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Quantization without tears

Reference 15

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Observation c2059587-3998-45f4-a800-a57abcff2a68 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Gonzalez, Hao Zhang, and Ion Stoica

Reference 16

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Observation 6e197e0b-d06b-422f-aeed-f85aa2c2caf7 · outbound

This paper cites ReActNet : Towards precise binary neural network with generalized activation functions.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization ReActNet : Towards precise binary neural network with generalized activation functions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:36.335290Z

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Observation 69445254-0874-48c1-90ce-a2958cc16f86 · outbound

This paper cites Network quantization with element-wise gradient scaling.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Network quantization with element-wise gradient scaling

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:36.159674Z

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Observation 1e376c2c-b290-4128-a595-83c64bb3555f · outbound

This paper cites Lsq+: Improving low-bit quantization through learnable offsets and better initialization.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Lsq+: Improving low-bit quantization through learnable offsets and better initialization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:36.012992Z

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Observation 2571cfb3-c535-4fcd-9681-b178f046b461 · outbound

This paper cites PTQ4ViT : Post-training quantization for Vision Transformers with twin uniform quantization.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization PTQ4ViT : Post-training quantization for Vision Transformers with twin uniform quantization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:35.843247Z

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Observation 97888d78-2694-4975-9f84-60a7432d6cad · outbound

This paper cites GPTQ : Accurate post-training quantization for generative pre-trained Transformers.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization GPTQ : Accurate post-training quantization for generative pre-trained Transformers

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:35.647849Z

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Observation 6d8f1073-51c7-42e1-96d8-e8c0da60b90e · outbound

This paper cites Up or down? adaptive rounding for post-training quantization.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Up or down? adaptive rounding for post-training quantization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:35.486397Z

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Observation a3c2b4c7-3ad5-40ab-a82e-dcc2da767388 · outbound

This paper cites QDROP : Randomly dropping quantization for extremely low-bit post-training quantization.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization QDROP : Randomly dropping quantization for extremely low-bit post-training quantization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:35.246530Z

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Observation 864558e2-d857-4e03-889b-2bfaf9ebc329 · outbound

This paper cites RepQ-ViT : Scale reparameterization for post-training quantization of Vision Transformers.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization RepQ-ViT : Scale reparameterization for post-training quantization of Vision Transformers

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:35.031648Z

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Observation 5d5659da-5a99-44be-a505-53863fac6dec · outbound

This paper cites FQ-ViT : Post-training quantization for fully quantized Vision Transformer.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization FQ-ViT : Post-training quantization for fully quantized Vision Transformer

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:34.831029Z

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Observation db53855a-a38a-4e7e-91ef-9b48a43761dc · outbound

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

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 26

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Observation 9515ccd6-cdb4-42eb-b009-dce72f1668ec · outbound

This paper cites TensorFlow Lite , 2024.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization TensorFlow Lite , 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:34.424066Z

Source-reported events for the cited work

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Observation 8f6cc431-7cb6-4552-9ae2-08d726a52278 · outbound

This paper cites Fully quantized network for object detection.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Fully quantized network for object detection

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:34.254252Z

Source-reported events for the cited work

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Observation 5bb2d7e5-5ac2-486e-9677-1d02f1727e3a · outbound

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

QwT-v2: Practical, Effective and Efficient Post-Training Quantization ImageNet : A large-scale hierarchical image database

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:34.103714Z

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Observation f32411ab-91dc-4140-995e-3346f0398c18 · outbound

This paper cites Swin Transformer : Hierarchical Vision Transformer using shifted windows.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Swin Transformer : Hierarchical Vision Transformer using shifted windows

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:33.992167Z

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

Observation 2ea925b2-c59d-49d2-9708-3d372d45a928 · outbound

This paper cites Deep residual learning for image recognition.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Deep residual learning for image recognition

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:33.861109Z

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

Observation 261e7f1a-20c0-445c-9395-f0875f975633 · outbound

This paper cites Microsoft COCO : Common objects in context.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Microsoft COCO : Common objects in context

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:33.670528Z

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

Observation 020581cb-d1bd-4fed-a4f6-1a1b5633e7d9 · outbound

This paper cites Cascade R-CNN : Delving into high quality object detection.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Cascade R-CNN : Delving into high quality object detection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:33.404125Z

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Observation 0b6d8fbe-cf64-4620-9248-fb7cad2dc972 · outbound

This paper cites The Llama 3 Herd of Models.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization The Llama 3 Herd of Models

Reference 34

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Observation b37936e4-6a91-4322-b4fd-75024189ecc4 · outbound

This paper cites Pointer sentinel mixture models.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Pointer sentinel mixture models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:33.133171Z

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Observation 9e1f3eb1-e906-4bd8-a798-e14637eaf4f0 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text Transformer.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Exploring the limits of transfer learning with a unified text-to-text Transformer

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:32.959648Z

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Observation 65ee4725-d53b-41e9-ade1-34afd2461b48 · outbound

This paper cites Social IQ a: Commonsense reasoning about social interactions.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Social IQ a: Commonsense reasoning about social interactions

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:32.719330Z

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 1558af1f-35ec-4d13-ac48-5c7bca839732 · outbound

This paper cites HellaSwag : Can a machine really finish your sentence? In Annual Meeting of the Association for Computational Linguistics, page 4791–4800, 2019.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization HellaSwag : Can a machine really finish your sentence? In Annual Meeting of the Association for Computational Linguistics, page 4791–4800, 2019

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:32.494230Z

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 034d7b00-0d5d-400c-94bd-17149f188d0d · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Piqa: Reasoning about physical commonsense in natural language

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:32.236253Z

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 9d0dd65c-e781-48d0-98c5-f96a9011ec6e · outbound

This paper cites WinoGrande : an adversarial winograd schema challenge at scale.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization WinoGrande : an adversarial winograd schema challenge at scale

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:31.951343Z

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 90169922-b5a9-4bd4-89f4-4e1b005d052e · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:30.079925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1acd8557-3bdb-4b21-957e-4e2eeeb4f4f1 · outbound

This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:31.614777Z

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 e477a98a-fe91-463a-95e5-454336a50bda · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:31.326005Z

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 e69ee5ae-43d8-4ce4-aea0-e9b5cf9cc67f · outbound

This paper cites Decoupled weight decay regularization.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Decoupled weight decay regularization

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:48:31.078741Z

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 e1633e1d-d337-422c-abc5-40a05b7c6ee3 · outbound

This paper cites Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers.

QwT-v2: Practical, Effective and Efficient Post-Training Quantization Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T13:48:30.736528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 865e6704-8ef8-4a51-9bf7-11c982512118 · 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 QwT-v2: Practical, Effective and Efficient Post-Training Quantization

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:11.926420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3089416c-90c4-4f02-a27e-80703dcc4c48 · inbound

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

Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization QwT-v2: Practical, Effective and Efficient Post-Training Quantization

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:59:01.884743Z

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.