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Paper Citation Record · LEDGER

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs

As of 10 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2505.17595.

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

pith.paper-citation-record.v1
2505.17595 v4

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:50:08.282552Z

measured 23 of 23 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

23 of 23 outbound references displayed

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  • verified fuzzy2
  • unresolved19
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  • malformed identifier2
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External citation measurements

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Outbound references

Observation 4f75b828-870c-46e8-9568-1c74961f2ca8 · outbound

This paper cites Implementation Details D.1.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs Implementation Details D.1

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:50:08.063676Z digest=sha256:c9b73405d4ab1a786893a52b491364bebbd8d59c6cb6b41ed6b4472f7df6f27a

Observation d60b96da-fd7b-4f3d-8139-70e186d87a7e · outbound

This paper cites Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al

Reference 5

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source=pdf_text observed=2026-08-07T14:50:06.555185Z digest=sha256:bfc74140bb8c4d2622edc60a5c4a05b4d5d6d4e71e64acbd1fbb618c4171936e

Observation 9756dc0e-73c0-42fe-b4af-2e9ebdf55320 · outbound

This paper cites The Llama 3 Herd of Models.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs The Llama 3 Herd of Models

Reference 6

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source=pdf_text observed=2026-08-07T14:50:06.697286Z digest=sha256:8fae2c4867f2ff4a5003d07e5625b73029f77096404def4e8885688ce22f7600

Observation 76dddf82-afb5-4854-bccc-a09f17123e90 · outbound

This paper cites OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting

Reference 7

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source=pdf_text observed=2026-08-07T14:50:06.797957Z digest=sha256:99e21be449ac386daefa60aac90cd3b8f2be768318d7a631b503c408ba31e817

Observation 7259daa3-bd41-49bb-9984-9964f8c2736e · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 9

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source=pdf_text observed=2026-08-07T14:50:07.031043Z digest=sha256:921acef6b1c344e4cff2f5c86247e07d392474ac9b9f7fde76bfa02ce1529dff

Observation bc70cfcb-1d21-44fe-8eb9-cf3058a6c137 · outbound

This paper cites S., and Solla, S.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs S., and Solla, S

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:50:07.142027Z digest=sha256:1628ba900ef0bad4d0ece603f3b9cd3f8dbc9f5c1389011011b1c3c89f52ee14

Observation 140a9275-d8b2-42de-8e43-c97d3e00c5db · outbound

This paper cites MQBench: Towards Reproducible and Deployable Model Quantization Benchmark.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Reference 12

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source=pdf_text observed=2026-08-07T14:50:07.377873Z digest=sha256:29dd62bc0e280fe05733e94c2ad0b189f5bdca9e7121f45a4b331ee7070372b2

Observation b916734e-53dd-4b04-9cc4-c8e8cc7594cc · outbound

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

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration

Reference 13

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source=pdf_text observed=2026-08-07T14:50:07.468226Z digest=sha256:7d8319a60ad8068407cb33162ad1dbde051f5f01a3d979bc670769628675f320

Observation 09101149-bf73-44f3-8eff-8674cd799c59 · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs SpinQuant: LLM quantization with learned rotations

Reference 14

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source=pdf_text observed=2026-08-07T14:50:07.556109Z digest=sha256:7a6cbde4475e7c7ee000ed5804bb0bfc71fdfb3a4563ff7ce7ab3f0c32021f8b

Observation 68f99cf3-cf03-475f-b1ed-75ac373b43e6 · outbound

This paper cites GPT-4 Technical Report.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs GPT-4 Technical Report

Reference 15

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source=pdf_text observed=2026-08-07T14:50:07.641903Z digest=sha256:f9141698365e66b0399989326f26eb394bcc42d44bab025b51744091a1812ca6

Observation 525e7bfe-1913-4952-ae7d-c07e4fb3d262 · outbound

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

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 16

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source=pdf_text observed=2026-08-07T14:50:07.711373Z digest=sha256:9f451d4c132c6fff442149af2becf5bfd3896cbd7f4457788f1ae9195f9612b7

Observation 853b06e2-f64f-416b-8371-ce56aa945d59 · outbound

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

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 17

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source=pdf_text observed=2026-08-07T14:50:07.786512Z digest=sha256:4d7a4f2ecf65a26b6bf0a8c4d13cf8b4fd519e930fe07a92756e7d1ecb157622

Observation 6a251c92-ad19-44d4-b0a0-ec903c81ca76 · outbound

This paper cites Qwen2.5 Technical Report.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs Qwen2.5 Technical Report

Reference 18

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source=pdf_text observed=2026-08-07T14:50:07.846146Z digest=sha256:986c97d39cd7fbe343c5874d718c9175dc70c334c4e45a9105327e8b0a637ddc

Observation cc6fad06-ec54-41a6-9028-04ac7bb19165 · outbound

This paper cites MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization

Reference 20

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source=pdf_text observed=2026-08-07T14:50:07.984695Z digest=sha256:58721be26ddb708cd5dd143732757e2ec64551ae26bbd5decb7035664d17b926

Observation d8c9ab5e-2fb2-4cee-b212-82cea659f651 · outbound

This paper cites an unresolved cited work.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs Unresolved cited work

Reference 128

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:50:08.282552Z digest=sha256:d13775641d8bc18203046776b1bc24d77472e81648cddd9c84dbc40541f54593

Observation 047a11fe-1364-4898-85c9-f4d066823795 · outbound

This paper cites TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction

Reference 1989

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source=pdf_text observed=2026-08-07T14:50:07.266451Z digest=sha256:e7ca3b75f7eb70a14d393d29a5d7813e01828833f32fe1a0d27feefc6764e108

Observation db4eae38-23c4-4b51-b496-0400ac2805f8 · outbound

This paper cites Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Reference 2017

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source=pdf_text observed=2026-08-07T14:50:06.925729Z digest=sha256:32ea71b119794bc7a169e65456c3ab47478da4d73b8bb7c8ae219cdd01db239b

Observation 52f9032f-3900-40bb-81cd-832cc7207f16 · outbound

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

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2018

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source=pdf_text observed=2026-08-07T14:50:06.150368Z digest=sha256:20413b43ff4e939e12baad0c865f826060b0f54da258e4897df3b909c79736b1

Observation be871f21-9571-4c1c-85ac-15d8ef8a2866 · outbound

This paper cites doi: 10.18653/v1/P19-1472.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs doi: 10.18653/v1/P19-1472

Reference 2019

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source=pdf_text observed=2026-08-07T14:50:07.902092Z digest=sha256:f5d716ec60bbe8cf9cdaf66161cf8c5066e5b37188db6e0a24876730faf39d28

Observation 538f023b-9e34-4eff-b6ec-1081217044a8 · outbound

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

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2023

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source=pdf_text observed=2026-08-07T14:50:06.422696Z digest=sha256:b7cfd116efd7b4aa029da1aeba5d6b406c78ba3fe9697b6fb2aa0b38c97226d1

Observation 72936c72-86d3-474d-a798-ee83436988df · outbound

This paper cites FrameQuant: Flexible Low-Bit Quantization for Transformers.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs FrameQuant: Flexible Low-Bit Quantization for Transformers

Reference 2024

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source=pdf_text observed=2026-08-07T14:50:06.070595Z digest=sha256:74b333fd9c4ebb69482d22dc0580f0fd4f7f9da5901e141367a5f753662d75b8

Observation 754ff55a-3068-4a04-8c72-3a71345608b0 · outbound

This paper cites CBQ: Cross-Block Quantization for Large Language Models.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs CBQ: Cross-Block Quantization for Large Language Models

Reference 2025

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source=pdf_text observed=2026-08-07T14:50:06.273698Z digest=sha256:6f30e659fac82983a09db4ac6798c21ecb56768b51e55ba7707c8ff94615cb8d

Observation 1ec4d93a-aaa7-4ec6-9a0c-a61986114558 · outbound

This paper cites In contrast, Int-Search employs a grid search over candidate scale values, with the zero-point constrained to be a k-bit unsigned integer.

NeUQI: Near-Optimal Uniform Quantization Parameter Initialization for Low-Bit LLMs In contrast, Int-Search employs a grid search over candidate scale values, with the zero-point constrained to be a k-bit unsigned integer

Reference 2048

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:50:08.138889Z digest=sha256:dc0a8920f1bcce2393a0321e93a1dce4f73bb1bd064cf60429a6a299d83c75e7

Pith citing papers

No inbound Pith citation observations are available.