Paper Citation Record · LEDGER

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling

As of 24 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2506.05432.

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

pith.paper-citation-record.v1
2506.05432 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:42:41.058953Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T17:36:45.807397Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T15:05:48.304013Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy7
unresolved32
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 77feadb7-f16e-4fb1-bfb7-58212b2c036a · outbound

This paper cites QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.150220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c83fdad-a273-4b11-9aca-f80084270562 · outbound

This paper cites Springer, 2006.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Springer, 2006

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.193491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3cd9129-7392-48f8-afcc-1814dbcc29cd · outbound

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

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Piqa: Reasoning about physical commonsense in natural language

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:42:42.704729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f745a94a-2dc2-4285-8288-7fe73a9ab538 · outbound

This paper cites Language models are few-shot learners.Advances in Neural Information Processing Systems, 2020.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Language models are few-shot learners.Advances in Neural Information Processing Systems, 2020

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:42:42.549702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation efa06f60-b6b4-44b7-bd06-b992143f5a17 · outbound

This paper cites QuIP: 2-Bit Quantization of Large Language Models With Guarantees.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling QuIP: 2-Bit Quantization of Large Language Models With Guarantees

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.408599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65ff2377-1a4c-40bb-821d-6c4448a65f62 · outbound

This paper cites DB-LLM: Accurate Dual-Binarization for Efficient LLMs.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling DB-LLM: Accurate Dual-Binarization for Efficient LLMs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.497757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98cdbf25-e7ca-4a83-a0b5-1f2ba0da1451 · outbound

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

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.594495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6d2eb58-27e9-42fc-98ce-845f760e9934 · outbound

This paper cites High-dimensional data analysis: The curses and blessings of dimension- ality.AMS math challenges lecture, 1(2000):32, 2000.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling High-dimensional data analysis: The curses and blessings of dimension- ality.AMS math challenges lecture, 1(2000):32, 2000

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:42:42.377588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bb9922a0-e0f7-4fd0-a282-f63fb37a2d3d · outbound

This paper cites Extreme Compression of Large Language Models via Additive Quantization.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Extreme Compression of Large Language Models via Additive Quantization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.742493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9accdd8e-7239-4bfc-ad5f-58ba0616e133 · outbound

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

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.831047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7355537e-f287-4ac0-a9a0-9bdb7d08d75d · outbound

This paper cites The Llama 3 Herd of Models.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling The Llama 3 Herd of Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.930787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92b5cf37-42dd-4f41-a873-d814714ce93d · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.935828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5dee0307-c35e-4ce4-b769-d90f1e4214fe · outbound

This paper cites PolarQuant: Quantizing KV Caches with Polar Transformation.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling PolarQuant: Quantizing KV Caches with Polar Transformation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.940278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df8f646e-ae02-41d8-ab63-a56f18a35206 · outbound

This paper cites Springer, 2009.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Springer, 2009

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.944392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f427d71-a399-4caf-ab73-9d9024678da8 · outbound

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

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.948535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58dbbd83-93c5-4bcd-babd-474fe1a75da8 · outbound

This paper cites Mistral 7B.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Mistral 7B

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.953006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b03f9886-c7f3-4fa9-8266-74c26748c327 · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.957048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a24bff5-3514-4f26-a709-fb7cdfb44d03 · outbound

This paper cites DeepSeek-V3 Technical Report.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling DeepSeek-V3 Technical Report

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.961246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e95b400-b5b2-44c3-9a24-dec549b2a07b · outbound

This paper cites VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.966175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a41a4aa0-c380-4300-8003-e293a50b341c · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling SpinQuant: LLM quantization with learned rotations

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.970503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c96741e5-555a-4b48-995f-3f8bd82a27ff · outbound

This paper cites Least squares quantization in pcm.IEEE transactions on information theory, 28(2):129–137, 1982.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Least squares quantization in pcm.IEEE transactions on information theory, 28(2):129–137, 1982

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.975369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95120425-5f40-4c14-9bcf-46db8227eff6 · outbound

This paper cites Pointer Sentinel Mixture Models.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Pointer Sentinel Mixture Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.979588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaa4f898-1e39-473b-a0ec-55dbfbc31516 · outbound

This paper cites The expectation-maximization algorithm.IEEE Signal processing magazine, 13(6):47–60, 1996.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling The expectation-maximization algorithm.IEEE Signal processing magazine, 13(6):47–60, 1996

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.984563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46e4cec2-4e2a-4b09-9c58-68bcf3d65a22 · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in Neural Information Processing Systems, 2022.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Training language models to follow instructions with human feedback.Advances in Neural Information Processing Systems, 2022

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.989468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efa484bf-39e2-458c-9c9f-4d9fda291f58 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of Machine Learning Research, 2020.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of Machine Learning Research, 2020

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:42:42.209435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4b0891e0-38c5-419e-8c05-e8f890cf4882 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 2021.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 2021

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:40.998001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ec074b6-d5d3-4422-8d46-50b1ce5b3d22 · outbound

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

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.002418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 894f70f8-a44a-49c1-a02f-28aeb14e7723 · outbound

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

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling LLaMA: Open and Efficient Foundation Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.007059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c471131f-b2f9-4839-bf6b-3434ede8d010 · outbound

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

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.012111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9803ddb-fe65-4a43-a073-ffdc263e8117 · outbound

This paper cites QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.017031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ea29d85-0d1f-440b-8c59-304c540ce1c9 · outbound

This paper cites Qtip: Quantization with trellises and incoherence processing.Advances in Neural Information Processing Systems, 37:59597–59620, 2024.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling Qtip: Quantization with trellises and incoherence processing.Advances in Neural Information Processing Systems, 37:59597–59620, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:42:42.037001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7d5af0bd-31da-4270-8928-72422b7c89eb · outbound

This paper cites GPTVQ: The Blessing of Dimensionality for LLM Quantization.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling GPTVQ: The Blessing of Dimensionality for LLM Quantization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.028100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67fcdf88-79b7-4a46-9872-13c3f258bfb5 · outbound

This paper cites The sphere packing problem in dimension 8.Annals of mathematics, pages 991–1015, 2017.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling The sphere packing problem in dimension 8.Annals of mathematics, pages 991–1015, 2017

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:42:41.819845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 16d4aaef-b5d3-4e63-b52a-9d995918a227 · outbound

This paper cites PolarQuant: Leveraging Polar Transformation for Efficient Key Cache Quantization and Decoding Acceleration.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling PolarQuant: Leveraging Polar Transformation for Efficient Key Cache Quantization and Decoding Acceleration

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.037052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0f195fe-44ed-4d22-bec7-4466a1402886 · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.041348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a138f024-ee03-4bc0-97c7-7b1d9e2e4d83 · outbound

This paper cites ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.045802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 022c3336-4905-4dd7-a1a7-19febbec237a · outbound

This paper cites LLMViewer.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling LLMViewer

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:42:41.618895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 07300448-297a-413c-a67d-91aef5f612f8 · outbound

This paper cites WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.053905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8f6357b-46f0-4750-bb33-fabbd7c6cef6 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.058953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e0a628e1-eaf9-4960-a1c4-526bf832838c · inbound

MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization cites this paper.

MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:05:48.305658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.