Pith. sign in

Paper Citation Record · LEDGER

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

As of 16 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

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

23 of 23 outbound references displayed

  • verified exact0
  • verified fuzzy2
  • unresolved19
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:50:09.041747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:50:08.063676Z digest=sha256:6668f719ee5a5d94744864da1e2ee9a1eae4ddf40b15a18eadb7bc628a5fc34b

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:06.555185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:06.555185Z digest=sha256:24c3f8588ba9eb0e6fe1c2145740841ff9ac719fa37bb8d911dc99b8fdae3243

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:06.697286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:06.697286Z digest=sha256:9c5aa82404d158f792a65aac1e3250675398d5b75b66423d26b2c4ced2f1245e

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:06.797957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:06.797957Z digest=sha256:b5d9c4d9c4b81eb41f6f9655b121e7dd11eed90ee1b2e942faa1ed75c3c8801c

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.031043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.031043Z digest=sha256:04dad90d41ab3c7b6973bf14c1678f08c1304895d1e448d9042a4a7a04431e7e

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:50:09.164691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:50:07.142027Z digest=sha256:ac7ed40ab31bbaf5b85528b63f13e7942cf528e7af04ac0febed81844ddfc2fb

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.377873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.377873Z digest=sha256:6d4f60c43c9d65a4d4785bc71d74ebb64cc39dafb9daf8ee43baea74249e5717

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.468226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.468226Z digest=sha256:f4cf3d250c897c0e75e205f2e7d28ee68a795e1564e187260c4f686c98e7a4d0

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.556109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.556109Z digest=sha256:3b016c2836d8b2804eaaac58865fab4eb8f146cbe7b8557af8bf324e02ebd264

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.641903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.641903Z digest=sha256:3cb6337465a060beee1552fd9cefa5c362484741c58f3dd68ba955816735e59e

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.711373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.711373Z digest=sha256:99a880cbaef19711cee6a0ee5904dbca0b3bdb3941efee45c795e779b12ab2ce

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.786512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.786512Z digest=sha256:b258f773a8be2ac0146c6c10c700193708d1e5033a32e2cd7d6c869b2529d82c

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.846146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.846146Z digest=sha256:806a9037adf92aff0560c5c37aaa4f3301576b20c5be4b673d4565ebd8392aa2

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.984695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.984695Z digest=sha256:8535355c075d29860dd34548f2d1120c9f9d70cbc40374c5034b9ad9bdfa1aa6

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

Resolution
malformed identifier
raw_fallback, observed 2026-08-07T14:50:08.557498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:50:08.282552Z digest=sha256:7e785cfcda37d8e7c982ec5e3b8092b8b94d598e4bcf2e216b5db82bc82f2925

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.266451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.266451Z digest=sha256:1e693eaed8e5a46d193e9f675b66ef2a3b3c9c349e1c8b7d20b8306a4f77b4ea

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:06.925729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:06.925729Z digest=sha256:9b278ac20ff51c174b90f2c67cb7f58da5b6b44e05cca9a841eebac039ff9954

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:06.150368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:06.150368Z digest=sha256:dea9bcc825a90ce15829f1eb163005a1221309905e5212c9cc3ba14fcdb51f63

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:07.902092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:07.902092Z digest=sha256:a1abfb4712e0e650e076fa52147bc0c42d92185eff9ced173cca68b1508d95c1

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:06.422696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:06.422696Z digest=sha256:ecc6ab768a5253d0489eea31839be1f2cf23101184cd6e6361b65859e13d91bb

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:06.070595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:06.070595Z digest=sha256:cd271bb58547167f730324c6f9f086897727e4127881ba40513a4c4a0e139122

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

Resolution
unresolved
no resolver link, observed 2026-08-07T14:50:06.273698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:50:06.273698Z digest=sha256:6d573c21940c9e90113b7606fceab49f9e216380f0df53be5f5e84dcdb34cd26

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

Resolution
malformed identifier
raw_fallback, observed 2026-08-07T14:50:08.728718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:50:08.138889Z digest=sha256:0d4ee1815df076ab8deb9a1132c7a02962e6510da74ce298a2792ab8e46de5dd

Pith citing papers

No inbound Pith citation observations are available.