Channel-wise Vector Quantization | Paper Citation Record

Observation cd210e34-18b4-4cf5-8613-34906bdc6a51 · outbound

This paper cites GPT-4 Technical Report.

Channel-wise Vector Quantization GPT-4 Technical Report

Reference 1

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Observation d1d8ae2d-1018-490a-97eb-b56d166c321c · outbound

This paper cites Flextok: Resampling images into 1d token sequences of flexible length.

Channel-wise Vector Quantization Flextok: Resampling images into 1d token sequences of flexible length

Reference 2

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Observation 917cfd70-ade0-42f6-b3a5-f434e01513f9 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 2020.

Channel-wise Vector Quantization Language models are few-shot learners.Advances in neural information processing systems, 2020

Reference 3

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Observation ddf73cb8-261a-4544-b022-41bed96f0b7e · outbound

This paper cites Maskgit: Masked generative image transformer.

Channel-wise Vector Quantization Maskgit: Masked generative image transformer

Reference 4

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Observation 150d572f-96f6-43ad-8895-0c461b2754c2 · outbound

This paper cites Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Channel-wise Vector Quantization Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 5

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Observation 0a66aa7d-e2f2-4225-b48b-c78eaf1e8f9d · outbound

This paper cites BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset.

Channel-wise Vector Quantization BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:44:01.503313Z

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Observation e265057f-8abc-4bb4-b5ef-bfd0fd7a620d · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

Channel-wise Vector Quantization PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 7

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Observation e220561b-f83a-4e5e-a233-4b249e0f795f · outbound

This paper cites Catok: Taming mean flows for one-dimensional causal image tokenization.

Channel-wise Vector Quantization Catok: Taming mean flows for one-dimensional causal image tokenization

Reference 8

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Observation c9587df6-6169-4e09-a27e-544bb6aa8b7a · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

Channel-wise Vector Quantization Imagenet: A large- scale hierarchical image database

Reference 9

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Observation bf9fb3de-d546-4628-a596-cbb41fe74fc4 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Channel-wise Vector Quantization Taming transformers for high-resolution image synthesis

Reference 10

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Observation 6c30c0a8-5b83-4207-96c5-17db729dedae · outbound

This paper cites Scaling rectified flow transform- ers for high-resolution image synthesis.

Channel-wise Vector Quantization Scaling rectified flow transform- ers for high-resolution image synthesis

Reference 11

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Observation 467ec79d-708d-48d6-aeed-68e0cf188c08 · outbound

This paper cites Restructuring vector quantization with the rotation trick.

Channel-wise Vector Quantization Restructuring vector quantization with the rotation trick

Reference 12

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Observation d8e7ea1d-c731-4451-8f8d-9e0992f4c48a · outbound

This paper cites Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis.

Channel-wise Vector Quantization Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis

Reference 13

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Observation 89dd5760-c5e4-4f74-835d-f56b0a1203b9 · outbound

This paper cites Flowtok: Flowing seamlessly across text and image tokens.

Channel-wise Vector Quantization Flowtok: Flowing seamlessly across text and image tokens

Reference 14

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Observation 7f52f454-f1e3-43b5-8d39-f60e454880f5 · outbound

This paper cites Statistics of patch offsets for image completion.

Channel-wise Vector Quantization Statistics of patch offsets for image completion

Reference 15

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Observation 63279dcb-b2ef-4916-8ecc-8978acae8c2d · outbound

This paper cites Towards accurate image coding: Improved autoregressive image generation with dynamic vector quantization.

Channel-wise Vector Quantization Towards accurate image coding: Improved autoregressive image generation with dynamic vector quantization

Reference 16

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Observation 37518384-0aee-4ea4-9e17-a16b06d92ce2 · outbound

This paper cites Spec- tralar: Spectral autoregressive visual generation.

Channel-wise Vector Quantization Spec- tralar: Spectral autoregressive visual generation

Reference 17

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Observation e3638e2d-55a1-4fdd-a148-1d03a39acab4 · outbound

This paper cites Nfig: Au- toregressive image generation with next-frequency predic- tion.

Channel-wise Vector Quantization Nfig: Au- toregressive image generation with next-frequency predic- tion

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.500686Z

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Observation 13496c26-f688-473f-8f4e-84df637ad9c5 · outbound

This paper cites Image-to-image translation with conditional adversarial networks.

Channel-wise Vector Quantization Image-to-image translation with conditional adversarial networks

Reference 19

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Observation fd363285-7cd0-4628-ab96-e87429ad378a · outbound

This paper cites Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens.

Channel-wise Vector Quantization Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.487565Z

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Observation de56f47e-a47d-4854-9efb-05349ad5954d · outbound

This paper cites Flux.https://github.com/black-forest-labs/flux, 2024.

Channel-wise Vector Quantization Flux.https://github.com/black-forest-labs/flux, 2024

Reference 21

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Observation ea67b4ea-26a7-4517-a771-8c6798774ba9 · outbound

This paper cites Autoregressive image generation using residual quantization.

Channel-wise Vector Quantization Autoregressive image generation using residual quantization

Reference 22

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Observation 83e4063b-2ef3-431a-93ca-f2266ce8915c · outbound

This paper cites Infini- tystar: Unified spacetime autoregressive modeling for visual generation.

Channel-wise Vector Quantization Infini- tystar: Unified spacetime autoregressive modeling for visual generation

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.490406Z

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Observation 8b52e960-413c-4d03-9c8e-b2812c1f9336 · outbound

This paper cites Unitok: A unified tokenizer for visual generation and understanding.arXiv preprint arXiv:2502.20321, 2025a.

Channel-wise Vector Quantization Unitok: A unified tokenizer for visual generation and understanding.arXiv preprint arXiv:2502.20321, 2025a

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.481719Z

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Observation 7a37defd-297e-42c0-b33f-c85ea02ddd0d · outbound

This paper cites STAR: Scale-wise Text-conditioned AutoRegressive image generation.

Channel-wise Vector Quantization STAR: Scale-wise Text-conditioned AutoRegressive image generation

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.474030Z

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Observation 83302f3f-2fab-4177-b933-a3a3cf8ec55f · outbound

This paper cites Finite scalar quantization: VQ-V AE made simple.

Channel-wise Vector Quantization Finite scalar quantization: VQ-V AE made simple

Reference 26

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Observation 6de7f220-6157-4ad9-87a7-2d03a3dc402b · outbound

This paper cites Randar: Decoder-only autoregressive visual generation in random orders.

Channel-wise Vector Quantization Randar: Decoder-only autoregressive visual generation in random orders

Reference 27

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Observation 385fce98-6a59-43d2-8c3a-5786463f058b · outbound

This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis.The Twelfth International Conference on Learning Representations, 2024.

Channel-wise Vector Quantization Sdxl: Improving latent diffusion models for high-resolution image synthesis.The Twelfth International Conference on Learning Representations, 2024

Reference 28

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Observation d40d8fbb-c1ad-44b9-8a7e-3532c9c759c2 · outbound

This paper cites Tokenflow: Unified image tokenizer for multimodal understanding and generation.

Channel-wise Vector Quantization Tokenflow: Unified image tokenizer for multimodal understanding and generation

Reference 29

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Observation 6323503a-73c7-4391-880c-fe3bc36d5b8d · outbound

This paper cites Learning ordered representations with nested dropout.

Channel-wise Vector Quantization Learning ordered representations with nested dropout

Reference 30

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Observation a2939349-d4fe-45ba-9edf-bd5941f82dd3 · outbound

This paper cites Scalable image tokenization with index backpropagation quantization.

Channel-wise Vector Quantization Scalable image tokenization with index backpropagation quantization

Reference 31

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Observation 2f137293-6aee-4727-90f0-7a28cb988c75 · outbound

This paper cites DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies.

Channel-wise Vector Quantization DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:44:01.476466Z

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Observation 64a2410f-7f28-4c27-8bd1-4119084bc62d · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Channel-wise Vector Quantization Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:44:01.471286Z

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Observation 315b243f-f075-4dea-ace3-92ba40bb96fd · outbound

This paper cites Hart: Efficient visual generation with hybrid autore- gressive transformer.The Thirteenth International Conference on Learning Representations,.

Channel-wise Vector Quantization Hart: Efficient visual generation with hybrid autore- gressive transformer.The Thirteenth International Conference on Learning Representations,

Reference 34

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Observation bcea00a4-4113-42cb-886f-559d71d6d319 · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

Channel-wise Vector Quantization Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:44:01.468948Z

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Observation 19dfa07d-2dbc-49ff-a6ca-17a5f9b46735 · outbound

This paper cites NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale.

Channel-wise Vector Quantization NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.484864Z

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Observation e61dec14-07a6-4593-a9d3-defa6fdebe0b · outbound

This paper cites Visual autoregressive modeling: Scalable image generation via next-scale prediction.Advances in neural information processing systems, 2024.

Channel-wise Vector Quantization Visual autoregressive modeling: Scalable image generation via next-scale prediction.Advances in neural information processing systems, 2024

Reference 37

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Observation aa4ab801-8aa8-4406-97be-7d05c3b1374f · outbound

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

Channel-wise Vector Quantization LLaMA: Open and Efficient Foundation Language Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:44:01.497938Z

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Observation 5777cad5-6ed1-4017-8287-91cfc083e435 · outbound

This paper cites Neural discrete representation learning.

Channel-wise Vector Quantization Neural discrete representation learning

Reference 39

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Observation e27fc053-a4f6-41a1-a85a-1c4bbef71f2c · outbound

This paper cites Switti: Designing scale-wise transformers for text-to-image synthesis.

Channel-wise Vector Quantization Switti: Designing scale-wise transformers for text-to-image synthesis

Reference 40

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Observation 8b8c3d18-92b6-44dd-a8d9-56aa6ba2d666 · outbound

This paper cites Emu3: Next-Token Prediction is All You Need.

Channel-wise Vector Quantization Emu3: Next-Token Prediction is All You Need

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:44:01.493026Z

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Observation 10b76b22-5bbc-4062-b435-fcc1ee231a17 · outbound

This paper cites Principal components.

Channel-wise Vector Quantization Principal components

Reference 42

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Observation e563c129-880c-4828-ade4-d99ff8b3696f · outbound

This paper cites Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation.

Channel-wise Vector Quantization Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:44:01.495621Z

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Observation 5eef5f86-a958-4f0f-924e-5dd265268aa1 · outbound

This paper cites Liquid: Language models are scalable and unified multi-modal generators.

Channel-wise Vector Quantization Liquid: Language models are scalable and unified multi-modal generators

Reference 44

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Observation 4dc9777a-b733-400b-866c-f8e1c526877c · outbound

This paper cites Vila-u: a unified foundation model integrating visual understanding and generation.The Thirteenth International Conference on Learning Representations, 2025.

Channel-wise Vector Quantization Vila-u: a unified foundation model integrating visual understanding and generation.The Thirteenth International Conference on Learning Representations, 2025

Reference 45

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Observation 518f376c-f637-4554-bbec-262f840a8544 · outbound

This paper cites Sana: Efficient high-resolution image synthesis with linear diffusion transformers.The Thirteenth International Conference on Learning Representations, 2025.

Channel-wise Vector Quantization Sana: Efficient high-resolution image synthesis with linear diffusion transformers.The Thirteenth International Conference on Learning Representations, 2025

Reference 46

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Observation dedd67dd-67a1-4060-857d-f90e62b4505a · outbound

This paper cites Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.

Channel-wise Vector Quantization Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:44:01.478776Z

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Observation abe4463c-d527-45c4-923b-128724876907 · outbound

This paper cites Muse-vl: Modeling unified vlm through semantic discrete encoding.

Channel-wise Vector Quantization Muse-vl: Modeling unified vlm through semantic discrete encoding

Reference 48

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Observation bb930f47-24bb-4aa4-ae0b-7a045cdf943c · outbound

This paper cites Anytime Sampling for Autoregressive Models via Ordered Autoencoding.

Channel-wise Vector Quantization Anytime Sampling for Autoregressive Models via Ordered Autoencoding

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.515429Z

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Observation 12186e8d-900f-485b-8dc5-8c62ba360810 · outbound

This paper cites Qwen3 Technical Report.

Channel-wise Vector Quantization Qwen3 Technical Report

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-06-29T22:44:01.466373Z

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Observation 19626b4a-cc49-440b-b899-261fbbac2195 · outbound

This paper cites Vector-quantized image modeling with improved vqgan.

Channel-wise Vector Quantization Vector-quantized image modeling with improved vqgan

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-29T22:40:05.761148Z

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Observation b08c9a38-5391-44b8-a5f3-439b2e044ac5 · outbound

This paper cites Language model beats diffusion - tokenizer is key to visual generation.

Channel-wise Vector Quantization Language model beats diffusion - tokenizer is key to visual generation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-29T22:40:05.761148Z

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Observation 5f6a41cb-3330-4d86-9eef-fa429d2f0005 · outbound

This paper cites An image is worth 32 tokens for reconstruction and generation.Advances in Neural Information Processing Systems, 2024.

Channel-wise Vector Quantization An image is worth 32 tokens for reconstruction and generation.Advances in Neural Information Processing Systems, 2024

Reference 53

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Observation 5c894afe-3774-46a5-9fa7-2f0d8f796a35 · outbound

This paper cites Randomized au- toregressive visual generation.

Channel-wise Vector Quantization Randomized au- toregressive visual generation

Reference 54

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Observation 78793e46-12f6-42f0-90da-ad212d7fd924 · outbound

This paper cites The unrea- sonable effectiveness of deep features as a perceptual metric.

Channel-wise Vector Quantization The unrea- sonable effectiveness of deep features as a perceptual metric

Reference 55

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Observation c36106c0-26ac-4405-aa5d-6c349cabd014 · outbound

This paper cites Holistic tokenizer for autoregressive image generation.

Channel-wise Vector Quantization Holistic tokenizer for autoregressive image generation

Reference 56

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Observation 12c27c56-20f8-45d5-9cd9-1f2128e1c010 · outbound

This paper cites Movq: Modulating quantized vectors for high-fidelity image generation.Advances in Neural Information Processing Systems, 35:23412–23425, 2022.

Channel-wise Vector Quantization Movq: Modulating quantized vectors for high-fidelity image generation.Advances in Neural Information Processing Systems, 35:23412–23425, 2022

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-29T22:40:05.761148Z

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Observation 5679ca10-a25a-4e73-9a49-5ef692be0c91 · outbound

This paper cites Scaling the codebook size of vq-gan to 100,000 with a utilization rate of 99%.

Channel-wise Vector Quantization Scaling the codebook size of vq-gan to 100,000 with a utilization rate of 99%

Reference 58

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Observation 9a26a862-a695-44e0-9beb-f48c231a8bd1 · outbound

This paper cites Addressing representation collapse in vector quantized models with one linear layer.

Channel-wise Vector Quantization Addressing representation collapse in vector quantized models with one linear layer

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-29T22:40:05.761148Z

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Observation 28fb7f48-9ed4-43ae-be87-a56507d34d44 · outbound

This paper cites VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning.

Channel-wise Vector Quantization VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.509628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 66690027-74de-42ad-979c-9c63d672cfe5 · outbound

This paper cites Lumina-next: Making lumina-t2x stronger and faster with next-dit.Advances in Neural Information Processing Systems, 2024.

Channel-wise Vector Quantization Lumina-next: Making lumina-t2x stronger and faster with next-dit.Advances in Neural Information Processing Systems, 2024

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-29T22:40:05.761148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc75725f-bc06-4197-a193-8ed5c7884ab7 · outbound

This paper cites an unresolved cited work.

Channel-wise Vector Quantization Unresolved cited work

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-29T22:40:05.761148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers