Paper Citation Record · LEDGER

Explaining How Quantization Disparately Skews a Model

As of 17 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2509.07222.

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

pith.paper-citation-record.v1
2509.07222 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:41:17.578542Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

12 of 12 outbound references displayed

verified exact1
verified fuzzy2
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f384917-85c9-4a98-a53a-d05f7833dd26 · outbound

This paper cites Un- covering and mitigating algorithmic bias through learned latent structure.

Explaining How Quantization Disparately Skews a Model Un- covering and mitigating algorithmic bias through learned latent structure

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:41:17.905826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 16223b5f-abe9-4dcb-9d2a-ab0e00d61238 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Explaining How Quantization Disparately Skews a Model Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T22:41:17.277620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b49f2f4-77c5-4abc-bcd6-bf59447a9fa5 · outbound

This paper cites On large-batch training for deep learning: Generalization gap and sharp minima.

Explaining How Quantization Disparately Skews a Model On large-batch training for deep learning: Generalization gap and sharp minima

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:41:17.829553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1f624ef5-dd75-4002-a990-92776cc5a6d2 · outbound

This paper cites A White Paper on Neural Network Quantization.

Explaining How Quantization Disparately Skews a Model A White Paper on Neural Network Quantization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T22:41:17.459798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 284ce55a-b07f-483a-b7f0-5d15a632bd2f · outbound

This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

Explaining How Quantization Disparately Skews a Model To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T22:41:17.578542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38df0666-0d2b-4379-837d-5fb119133533 · outbound

This paper cites Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction.

Explaining How Quantization Disparately Skews a Model Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-04T22:41:17.507653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96659789-0933-43bf-b38f-47269ae135d2 · outbound

This paper cites What Do Compressed Deep Neural Networks Forget?.

Explaining How Quantization Disparately Skews a Model What Do Compressed Deep Neural Networks Forget?

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-04T22:41:17.308780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a23ff971-4c8d-4fe3-8aab-1f592a335631 · outbound

This paper cites Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization.

Explaining How Quantization Disparately Skews a Model Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T22:41:17.395343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 062f522e-cca3-4ee2-b4ea-613acaf57a90 · outbound

This paper cites Efficient and Robust Quantization-aware Training via Adaptive Coreset Selection.

Explaining How Quantization Disparately Skews a Model Efficient and Robust Quantization-aware Training via Adaptive Coreset Selection

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-04T22:41:17.699800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f706f310-9207-4042-bbeb-09670ad286f7 · outbound

This paper cites Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search.

Explaining How Quantization Disparately Skews a Model Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T22:41:17.547175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d12c40e4-9993-43a0-b820-cfb1636c84f2 · outbound

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

Explaining How Quantization Disparately Skews a Model GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T22:41:17.246666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d2c7a5f-74bd-4e8d-b462-927597d79998 · outbound

This paper cites Pruning vs Quantization: Which is Better?.

Explaining How Quantization Disparately Skews a Model Pruning vs Quantization: Which is Better?

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T22:41:17.422775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.