Paper Citation Record · LEDGER

ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2303.08302.

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

pith.paper-citation-record.v1
2303.08302 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T01:00:02.087433Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T00:56:24.897034Z

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 f26d289c-09a8-4f5c-a6f8-ce0ad0956639 · inbound

FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance cites this paper.

FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:56:45.442680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f06b8139-c80f-4fba-aeb4-50b171d3cb12 · inbound

A Survey on Efficient Inference for Large Language Models cites this paper.

A Survey on Efficient Inference for Large Language Models ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 206

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:39:33.241090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 36f0a79b-4589-40f1-821d-06efef05c5b5 · inbound

Deploying Foundation Model Powered Agent Services: A Survey cites this paper.

Deploying Foundation Model Powered Agent Services: A Survey ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 212

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:46.750586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e13bac50-265a-49da-8a76-8133c812febf · inbound

MBQ: Modality-Balanced Quantization for Large Vision-Language Models cites this paper.

MBQ: Modality-Balanced Quantization for Large Vision-Language Models ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:52.305041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa207045-082f-4f89-a9bc-811e3d79261e · inbound

4bit-Quantization in Vector-Embedding for RAG cites this paper.

4bit-Quantization in Vector-Embedding for RAG ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T19:12:58.823451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46c4b673-a8f0-45b8-96fe-44e33b05dfd8 · inbound

iServe: An Intent-based Serving System for LLMs cites this paper.

iServe: An Intent-based Serving System for LLMs ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 123

Resolution: unresolved; no resolver link, observed 2026-08-10T21:37:14.549116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dae832e8-680d-4d9d-b77b-e0f4a1f3b475 · inbound

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization cites this paper.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.734518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f40fa136-bbbf-48b3-8d36-1fd779a4d639 · inbound

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques cites this paper.

Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T01:00:02.087433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c69edea0-5bfa-4fec-bba0-34479dc12a9b · inbound

Low-bit Model Quantization for Deep Neural Networks: A Survey cites this paper.

Low-bit Model Quantization for Deep Neural Networks: A Survey ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:19.805481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95b31ac0-1b8e-471c-a3b7-6cd45ee75356 · inbound

Resource-Efficient Language Models: Quantization for Fast and Accessible Inference cites this paper.

Resource-Efficient Language Models: Quantization for Fast and Accessible Inference ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T21:55:07.881424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ed274c0-c79e-4d6a-8a69-55a310c6f575 · inbound

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation cites this paper.

Enabling On-Device Medical AI Assistants via Input-Driven Saliency Adaptation ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:57:41.320727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7f95455-3a75-4a94-a33b-840c231f2507 · inbound

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models cites this paper.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.123982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25797477-e26a-421e-9ef0-e44c03e8bedc · inbound

BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity cites this paper.

BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T18:20:00.467026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d49f8fb0-a2f7-488c-890d-ab937df70595 · inbound

Towards Inclusive NLP: Assessing Compressed Multilingual Transformers across Diverse Language Benchmarks cites this paper.

Towards Inclusive NLP: Assessing Compressed Multilingual Transformers across Diverse Language Benchmarks ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T14:11:13.303967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef2bf2c0-6452-4e33-bb6e-4c5c73e30e2f · inbound

Enhancing Model Privacy in Federated Learning with Random Masking and Quantization cites this paper.

Enhancing Model Privacy in Federated Learning with Random Masking and Quantization ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T16:10:53.398370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8566c60f-50ec-4410-94e6-6776a6cd901a · inbound

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations cites this paper.

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-21T18:50:30.273755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9777d95e-76ab-4c39-b98f-ae42929a6f48 · inbound

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations cites this paper.

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-03T23:21:42.110167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 310bac23-4950-49a3-a4ed-0b616205c63b · inbound

MCAP: Deployment-Time Layer Profiling for Memory-Constrained LLM Inference cites this paper.

MCAP: Deployment-Time Layer Profiling for Memory-Constrained LLM Inference ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:54:48.415115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f05640c3-6400-4fc2-b46b-aacd0f9b81f3 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:36:19.950190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 03866696-993d-496c-93ff-04e990ad6929 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T07:32:30.196922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4246816d-2079-4874-b669-88e413c3a234 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T07:59:50.166136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bc7bccb3-9cea-42ea-8cce-3c2bf492175e · inbound

Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models cites this paper.

Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:23:03.633191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9e9c1558-3a9b-4696-8b17-2112027836db · inbound

ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression cites this paper.

ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:12:34.512202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 07652548-2994-4d7f-9f53-23a64a978848 · inbound

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation cites this paper.

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:06:14.396687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c504d84a-0b2d-4614-a750-898e16cfb00e · inbound

TwinQuant: Learnable Subspace Decomposition for 4-Bit LLM Quantization cites this paper.

TwinQuant: Learnable Subspace Decomposition for 4-Bit LLM Quantization ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 100

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T00:56:24.899516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 66defc3c-1c96-409c-be9e-3130e323c914 · inbound

Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems cites this paper.

Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T03:55:41.125011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b0249f8-bee6-47b8-9021-6f42da1652e7 · inbound

GyRot: Leveraging Hidden Synergy between Rotation and Fine-grained Group Quantization for Low-bit LLM Inference cites this paper.

GyRot: Leveraging Hidden Synergy between Rotation and Fine-grained Group Quantization for Low-bit LLM Inference ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-01T03:16:46.948303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.