Paper Citation Record · LEDGER

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models

As of 11 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2501.06218.

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

pith.paper-citation-record.v1
2501.06218 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:04:24.401658Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0e95b02a-3cea-4716-839d-c5a20ea82a63 · outbound

This paper cites On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes

Reference 1

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Observation eccb549d-b876-4a09-8b7a-a01afacf00ab · outbound

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

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 6

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Observation 3e945640-3430-49dd-b294-50f5800a4001 · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Imagen Video: High Definition Video Generation with Diffusion Models

Reference 9

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Observation 62d5fcf8-6490-4d05-a484-6ff260f10351 · outbound

This paper cites Return of Unconditional Generation: A Self-supervised Representation Generation Method.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Return of Unconditional Generation: A Self-supervised Representation Generation Method

Reference 11

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Observation 64e1295e-7d39-4963-bf94-e888a74929cb · outbound

This paper cites FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 12

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Observation 4e3d5ba4-48eb-4cbd-afbf-1ba9276e44bd · outbound

This paper cites QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models

Reference 13

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Observation 94c1288a-8471-4399-b83e-fbe763a5fbd2 · outbound

This paper cites Pseudo Numerical Methods for Diffusion Models on Manifolds.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Pseudo Numerical Methods for Diffusion Models on Manifolds

Reference 14

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Observation 4ca946c4-4411-4c38-b7ed-39cdefb7baaa · outbound

This paper cites Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization

Reference 15

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Observation 2802a145-c99a-41d4-bd95-abd405aaf012 · outbound

This paper cites EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models

Reference 16

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Observation aceb9973-42cc-4567-a2e8-ba0f7b567344 · outbound

This paper cites A White Paper on Neural Network Quantization.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models A White Paper on Neural Network Quantization

Reference 17

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Observation ae0008a0-eac1-4b55-b7b5-e534da3f3ca3 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 18

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Observation 49a18a28-247b-4a79-99e6-1d935daadd72 · outbound

This paper cites A Constructive Prediction of the Generalization Error Across Scales.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models A Constructive Prediction of the Generalization Error Across Scales

Reference 19

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Observation ebc04c5b-af99-4db9-b289-dd3e75613dee · outbound

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

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 21

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Observation 69797785-9ac0-443f-89a1-051f946c08e8 · outbound

This paper cites Improved Vector Quantized Diffusion Models.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Improved Vector Quantized Diffusion Models

Reference 24

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Observation 2e0629b6-c86a-4d3a-97e6-3e1099613780 · outbound

This paper cites Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Reference 25

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Observation 342ce1b7-8c9a-4f7d-8d85-6a3708edacf7 · outbound

This paper cites GIVT: Generative Infinite-Vocabulary Transformers.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models GIVT: Generative Infinite-Vocabulary Transformers

Reference 26

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Observation e9811bac-a8e1-4076-997c-5feed5c2e8e6 · outbound

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

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks

Reference 27

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Observation d695c211-04d4-4161-a469-a0fd0625e617 · outbound

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

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models GPTVQ: The Blessing of Dimensionality for LLM Quantization

Reference 28

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Observation 01709ccb-6750-47df-b859-964f60dcd42c · outbound

This paper cites MaskBit: Embedding-free Image Generation via Bit Tokens.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models MaskBit: Embedding-free Image Generation via Bit Tokens

Reference 29

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Observation 055707ab-0c07-45ed-b1f4-75872dfe7a32 · outbound

This paper cites Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Reference 30

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Observation dace6967-3b1b-43e4-97ad-0452e18ac76a · outbound

This paper cites PTQ4DiT: Post-training Quantization for Diffusion Transformers.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models PTQ4DiT: Post-training Quantization for Diffusion Transformers

Reference 31

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Observation bde9f99f-54d5-4615-a62a-91282222c84e · outbound

This paper cites Vector-quantized Image Modeling with Improved VQGAN.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Vector-quantized Image Modeling with Improved VQGAN

Reference 32

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Observation ec1ca818-d779-4d23-80bd-47d78f7af62e · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 33

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Observation 18763087-f095-4f55-9431-441906313c0b · outbound

This paper cites Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 34

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Observation 435b1eff-5102-4f91-b854-59a0aa800eab · outbound

This paper cites GLM-130B: An Open Bilingual Pre-trained Model.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models GLM-130B: An Open Bilingual Pre-trained Model

Reference 35

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Observation 64643b1b-8f71-4790-93c0-ee7d4258579d · outbound

This paper cites Fast Sampling of Diffusion Models with Exponential Integrator.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Fast Sampling of Diffusion Models with Exponential Integrator

Reference 36

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This paper cites A Survey on Model Compression for Large Language Models.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models A Survey on Model Compression for Large Language Models

Reference 37

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Unresolved cited work

Reference 38

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Unresolved cited work

Reference 39

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Observation 30a8e580-e5b2-49d8-9949-ad2d24030928 · outbound

This paper cites Llama- Gen is a discrete language model, similar to V AR in terms of its discrete representation space.

Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Llama- Gen is a discrete language model, similar to V AR in terms of its discrete representation space

Reference 40

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Observation 1d285cd3-fda4-4707-9146-fca4a7cd87c0 · outbound

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Unresolved cited work

Reference 41

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Reference 51

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Observation 93bd1ce4-715b-4c82-bf81-c83b3d4aa5a4 · outbound

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Our analysis shows that Top KLD consistently achieves the SOTA results across various bit settings

Reference 52

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Observation d4998466-7974-4de9-9ca1-d673894d55fb · outbound

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Deep Learning Scaling is Predictable, Empirically

Reference 2004

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Post-training quantization on diffusion models

Reference 2014

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Denoising Diffusion Implicit Models

Reference 2015

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Distilling the Knowledge in a Neural Network

Reference 2017

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 2019

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

Reference 2020

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Scaling Laws for Neural Language Models

Reference 2021

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Muse: Text-To-Image Generation via Masked Generative Transformers

Reference 2022

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2023

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Dissecting Bit-Level Scaling Laws in Quantizing Vision Generative Models Imagenet: A large-scale hierarchical image database

Reference 2024

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Pith citing papers

No inbound Pith citation observations are available.