ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization | Paper Citation Record

Observation f9a999b4-ce67-4c40-9b37-4e7720bdd04b · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.489522Z

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Observation d23b5f3e-f292-4262-af08-5d851712f629 · outbound

This paper cites Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.472585Z

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Observation d7ae933d-f365-45df-ad1f-27e7880235d5 · outbound

This paper cites GPT-4 Technical Report.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization GPT-4 Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.376014Z

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Observation b88375c9-4928-4dd2-a935-894194bb7a31 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.381491Z

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Observation 374af6bd-1403-4ac0-b8da-ddfbd35679f0 · outbound

This paper cites Qwen Technical Report.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Qwen Technical Report

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.386856Z

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Observation eab27945-79d9-4b32-b034-a44f35790bd7 · outbound

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

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization LLaMA: Open and Efficient Foundation Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.392424Z

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Observation 35e4f51a-a631-4770-9eb8-0586de4220ff · outbound

This paper cites GPT3.int8(): 8-bit matrix multiplication for transformers at scale.Advances in Neural Information Processing Systems, 35:30318–30332, 2022.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization GPT3.int8(): 8-bit matrix multiplication for transformers at scale.Advances in Neural Information Processing Systems, 35:30318–30332, 2022

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.455186Z

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Observation 51c99b10-e70d-4f5a-8615-f7108356aa5b · outbound

This paper cites PaLM: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization PaLM: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.438597Z

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Observation 84a79a61-c7c3-4ba5-b5ae-341feb404cea · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39, 2022.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39, 2022

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.407669Z

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Observation 2af567d0-ff26-4451-9286-1da9b5132aba · outbound

This paper cites Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.412149Z

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Observation e11ec3a4-2955-496c-bfec-1e832bfc21a0 · outbound

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

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.416839Z

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Observation c6266379-79a8-44da-aac0-a3987f7d33bf · outbound

This paper cites Pact: Parameterized clipping activation for quantized neural networks.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Pact: Parameterized clipping activation for quantized neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.385273Z

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Observation 30f9bc72-00f0-440c-a74a-2cad04e7fbd3 · outbound

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

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization AWQ: Activation-aware weight quantization for on-device llm compression and acceleration.Proceedings of Machine Learning and Systems (MLSys), 6:87–100, 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.370015Z

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Observation c8e4b698-a7c3-4bc5-ab33-d4463571eee3 · outbound

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

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization SmoothQuant: Accurate and efficient post-training quantization for large language models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.353523Z

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Observation 7323f871-6d26-4a46-bc85-105d1e6db4a4 · outbound

This paper cites Mahoney, and Kurt Keutzer.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Mahoney, and Kurt Keutzer

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.337198Z

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Observation 6b5f9112-f8e9-4085-b654-12b3b4b96779 · outbound

This paper cites Binarized neural networks.Advances in neural information processing systems, 29, 2016.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Binarized neural networks.Advances in neural information processing systems, 29, 2016

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.320918Z

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Observation 098e9307-079b-4c42-8fcd-332f2a7358f9 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.446026Z

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Observation 53861648-2603-47d4-8bb8-6b0fac147499 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.Advances in neural information processing systems, 36:10088–10115, 2023.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Qlora: Efficient finetuning of quantized llms.Advances in neural information processing systems, 36:10088–10115, 2023

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.451136Z

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Observation 0fe97c5f-134c-4797-89f8-b900c2cf036d · outbound

This paper cites LLM-QAT: Data-free quantization-aware training for large language models.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization LLM-QAT: Data-free quantization-aware training for large language models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.289223Z

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Observation 49d11cdb-cb75-49f6-b71b-fcd2693b9064 · outbound

This paper cites EfficientQAT: Efficient quantization-aware training for large language models.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization EfficientQAT: Efficient quantization-aware training for large language models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.271912Z

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Observation 1ade1da4-bc2b-499e-8106-d678795f6c21 · outbound

This paper cites Dl-qat: Weight-decomposed low-rank quantization-aware training for large language models.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Dl-qat: Weight-decomposed low-rank quantization-aware training for large language models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.255177Z

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 c1f3ebe0-8add-473a-9757-e35919d2cc26 · outbound

This paper cites PV-Tuning: Beyond straight-through estimation for extreme llm compression.Advances in Neural Information Processing Systems, 37:5074–5121, 2024.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization PV-Tuning: Beyond straight-through estimation for extreme llm compression.Advances in Neural Information Processing Systems, 37:5074–5121, 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.239206Z

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Observation c191bc2c-2f79-4c5f-bdcb-b7844e805c7f · outbound

This paper cites A survey of low-bit large language models: Basics, systems, and algorithms.Neural Networks, page 107856, 2025.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization A survey of low-bit large language models: Basics, systems, and algorithms.Neural Networks, page 107856, 2025

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.223460Z

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

Observation 6c49d06e-b41b-4982-9bd1-e962e2e8d010 · outbound

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

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.479635Z

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Observation fbb4eb6b-d828-490c-b822-fa729bbc168f · outbound

This paper cites GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.484974Z

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Observation 4308054e-ab15-4dfd-add8-2d7c37da9123 · outbound

This paper cites Accurate post training quantization with small calibration sets.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Accurate post training quantization with small calibration sets

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.490319Z

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Observation 439f5088-b73d-42b8-8a53-66098913059f · outbound

This paper cites Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.197266Z

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Observation d3ddad64-fbbe-4859-abe9-ee230dce8ed2 · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization SpinQuant: LLM quantization with learned rotations

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.500156Z

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Observation 9be23b84-5247-44eb-bf59-0f89ebf3aed1 · outbound

This paper cites FlexRound: Learnable rounding based on element-wise division for post-training quantization.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization FlexRound: Learnable rounding based on element-wise division for post-training quantization

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.177031Z

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Observation 9650a2d1-4b42-404f-8ec0-894a169906e9 · outbound

This paper cites Post-training 4-bit quantization of convolutional networks for rapid deployment.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Post-training 4-bit quantization of convolutional networks for rapid deployment

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.160167Z

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 223d4133-2600-42e4-8d4f-5ef434708dda · outbound

This paper cites OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.516738Z

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Observation b48a504a-0a11-4aea-a173-9aca0565517a · outbound

This paper cites Optimal brain compression: A framework for accurate post-training quantization and pruning.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Optimal brain compression: A framework for accurate post-training quantization and pruning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.521779Z

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Observation 62953276-201f-4513-8cb5-31bf4c9889b1 · outbound

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

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Up or down? adaptive rounding for post-training quantization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.133371Z

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Observation ccde4fbd-50e6-498a-8973-e63f9184830e · outbound

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

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization BRECQ: Pushing the limit of post-training quantization by block reconstruction

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.531480Z

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Observation 2e16e909-a548-4937-8431-40bc56aeea0a · outbound

This paper cites Qwen3 Technical Report.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Qwen3 Technical Report

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.536372Z

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Observation 0b761cae-5ff9-48bc-8a89-68913c04c820 · outbound

This paper cites The Llama 3 Herd of Models.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization The Llama 3 Herd of Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.541877Z

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Observation 969365a0-c46c-4019-ae01-79c005bb78dd · outbound

This paper cites an unresolved cited work.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-10T16:36:25.104124Z

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Observation c80ac50e-a6ee-4c11-883e-8e837493d477 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Pytorch: An imperative style, high-performance deep learning library

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.087154Z

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

Observation dec0aef9-b87f-4703-a80f-eccf3cfa66ea · outbound

This paper cites Pointer Sentinel Mixture Models.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Pointer Sentinel Mixture Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.557010Z

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Observation 7d56eff2-0ffa-454d-9c98-34f2909f0fbc · outbound

This paper cites Enhancing Chat Language Models by Scaling High-quality Instructional Conversations.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.562095Z

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Observation e1fd45c7-279c-4ad0-84ac-0171be6a4f97 · outbound

This paper cites NuminaMath: A large-scale math reasoning dataset.https://huggingface.co/datasets/AI-MO/NuminaMath-CoT, 2024.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization NuminaMath: A large-scale math reasoning dataset.https://huggingface.co/datasets/AI-MO/NuminaMath-CoT, 2024

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.069821Z

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

Observation e823e6e3-0958-433e-af83-e77bdf62b222 · outbound

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

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.572210Z

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Observation 52b5834c-c764-464a-87d7-2195751d00c7 · outbound

This paper cites BoolQ: Exploring the surprising difficulty of natural Yes/No questions.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization BoolQ: Exploring the surprising difficulty of natural Yes/No questions

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.052466Z

Source-reported events for the cited work

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Observation 7c743ffd-024e-4ef3-b3db-c319c04013bd · outbound

This paper cites C-Eval: A multi-level multi-discipline chinese evaluation suite for foundation models.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization C-Eval: A multi-level multi-discipline chinese evaluation suite for foundation models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.033561Z

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 e928512a-c64e-4308-ad71-fda51c5e1ad4 · outbound

This paper cites HellaSwag: Can a machine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800, 2019.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization HellaSwag: Can a machine really finish your sentence? InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4791–4800, 2019

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.591387Z

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Observation ec75ce57-0827-4924-9bce-e9159f1b2637 · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:25.000427Z

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 1723e583-cbf3-48dd-a525-6bd9f3b1114a · outbound

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

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.601158Z

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Observation ed37aa45-399b-455e-808f-4e575f886d26 · outbound

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

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization PIQA: Reasoning about physical commonsense in natural language

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:24.963152Z

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 b62522a7-a056-45e3-9905-64b60458dfc6 · outbound

This paper cites SocialIQA: Commonsense reasoning about social interactions.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization SocialIQA: Commonsense reasoning about social interactions

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:36:24.944210Z

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 7dd9b8b6-deb2-4af0-9eef-b8126e99902f · outbound

This paper cites WinoGrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization WinoGrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T16:36:24.615137Z

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Observation ba864ff9-3ec0-45ff-8243-6429f66c73c4 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 51

Resolution: malformed identifier; no resolver link, observed 2026-08-10T16:36:24.619756Z

Source-reported events for the cited work

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Coverage vector

Reference resolution

Outbound references

Pith citing papers