Paper Citation Record · LEDGER

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization

As of 22 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2412.04180.

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

pith.paper-citation-record.v1
2412.04180 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:45:16.659068Z

measured 20 of 20 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:07.982631Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:52:09.957159Z

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy0
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2d2739d7-f8ed-4813-a179-314fc46d0938 · outbound

This paper cites GPT-4 Technical Report.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.581616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d3a9be7-5266-4d31-ad8e-4ef0d1af519e · outbound

This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.596003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b2a672d-795a-46d7-aadf-b8c2db4bb3d0 · outbound

This paper cites The Llama 3 Herd of Models.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization The Llama 3 Herd of Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.604963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40843d43-a323-45b6-8917-d3d643d6472e · outbound

This paper cites decoupleQ: Towards 2-bit Post-Training Uniform Quantization via decoupling Parameters into Integer and Floating Points.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization decoupleQ: Towards 2-bit Post-Training Uniform Quantization via decoupling Parameters into Integer and Floating Points

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.612867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7264e2e6-4132-41cc-8805-38a9d349ece8 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization Measuring Massive Multitask Language Understanding

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.616535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 677cb0ea-fa72-48af-9a49-3fbd6ba5b39d · outbound

This paper cites Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.620506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cbae425-f249-4cdb-9cbd-9dade7b6bc17 · outbound

This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.633417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98d7c674-dee4-4c84-90cd-ea4462ed7424 · outbound

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

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.642389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61439cbf-36ce-4b32-85cd-8dc2f8c7119d · outbound

This paper cites NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.650660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a4fb913-f826-4dfb-ac3d-65135285ac08 · outbound

This paper cites ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.654951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50101564-ffb1-4151-9793-1b7a22d1377f · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization OPT: Open Pre-trained Transformer Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.659068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b292e447-fbcc-4c9f-b3a5-73b24fe4f44f · outbound

This paper cites Pointer Sentinel Mixture Models.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization Pointer Sentinel Mixture Models

Reference 1987

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.637968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72750342-0ec9-431b-bb65-30044df830a3 · outbound

This paper cites Crafting papers on machine learning.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization Crafting papers on machine learning

Reference 1999

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.629289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7621d1ce-c7a9-4454-835a-11f8a9a705e5 · outbound

This paper cites SqueezeLLM: Dense-and-Sparse Quantization.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization SqueezeLLM: Dense-and-Sparse Quantization

Reference 2002

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.624860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c9be1e0-e78e-47a6-a1ec-75075045aa3b · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.586772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cdf5b79-f138-48c9-bec1-89c09001fbeb · outbound

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

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization LLaMA: Open and Efficient Foundation Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.646581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e1d150e-5a5a-4291-bc79-33ac5a20896d · outbound

This paper cites doi: https://doi.org/10.1016/j.cor.2021.105692.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization doi: https://doi.org/10.1016/j.cor.2021.105692

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.591442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c1c0587-199f-4547-96e5-715063a3b1c5 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization PaLM-E: An Embodied Multimodal Language Model

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.600412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a713749e-a7f5-4bd0-9930-00e169aa13a0 · outbound

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

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T21:45:16.608622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation fcc83397-63b7-4828-b9d2-9a86a8d06783 · inbound

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation cites this paper.

LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T14:52:10.063397Z

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.