Paper Citation Record · LEDGER

VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2409.17066.

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

pith.paper-citation-record.v1
2409.17066 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:42:21.528632Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:06:27.023208Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9110e3fc-7361-49c7-aacd-a3cf0b772e18 · inbound

Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models cites this paper.

Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:13:13.869768Z

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 0c0a7fbb-d6e8-41a6-9cc4-bf018e610764 · inbound

NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache cites this paper.

NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T14:42:21.528632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling cites this paper.

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 2fcb54b3-2474-4049-9824-e9909a3cdf86 · inbound

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook cites this paper.

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T14:07:20.581851Z

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 651510c7-f053-4b8d-a861-bca739c13eaa · inbound

CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs cites this paper.

CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T19:09:15.339967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3969531e-7938-4d7b-93c4-6c8c4ae37317 · inbound

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models cites this paper.

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:14:12.445024Z

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 da817990-744b-41ab-9269-fa2802301efb · inbound

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation cites this paper.

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:04.757066Z

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 d20a115c-47a0-4f3b-974a-19f0f5440faa · inbound

Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis cites this paper.

Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 66

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T20:29:00.028432Z

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 bbb893b3-5c06-46f9-987f-3b532e72697d · 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 VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T15:05:48.323209Z

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 b4a84467-6149-4eb8-934c-a058ecdb8777 · inbound

EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture cites this paper.

EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T14:44:45.644253Z

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 f6258a96-8a5d-4983-901e-5d90996c43a8 · inbound

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection cites this paper.

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:06:27.025229Z

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 c2d799d4-e14a-4279-9782-43de7ac3ffcd · inbound

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection cites this paper.

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T11:24:38.295648Z

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.