Paper Citation Record · LEDGER

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

As of 9 August 2026, this Paper Citation Record lists 100 of 103 outbound references and 13 inbound Pith citation observations for arXiv:2502.05178.

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

pith.paper-citation-record.v1
2502.05178 v1

Coverage vector

measured 100 of 103 reference resolution

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measured 13 of 13 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.

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Reference resolution

100 of 103 outbound references displayed

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External citation measurements

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Outbound references

Observation 8bd6c170-fe81-48bd-a650-84f219af87a7 · outbound

This paper cites GPT-4 Technical Report.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation GPT-4 Technical Report

Reference 1

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This paper cites Soft-to-hard vector quantization for end-to-end learn- ing compressible representations.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Soft-to-hard vector quantization for end-to-end learn- ing compressible representations

Reference 2

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This paper cites Beit: Bert pre-training of image transformers.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Beit: Bert pre-training of image transformers

Reference 3

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This paper cites Fuyu-8b: A multimodal architecture for ai agents, 2023.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Fuyu-8b: A multimodal architecture for ai agents, 2023

Reference 4

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This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 5

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This paper cites PaliGemma: A versatile 3B VLM for transfer.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation PaliGemma: A versatile 3B VLM for transfer

Reference 6

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This paper cites Piqa: Reasoning about physical commonsense in nat- ural language.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Piqa: Reasoning about physical commonsense in nat- ural language

Reference 7

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This paper cites Maskgit: Masked generative image transformer.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Maskgit: Masked generative image transformer

Reference 8

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Training Deep Nets with Sublinear Memory Cost

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This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 11

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This paper cites Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks

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This paper cites Reproducible scaling laws for contrastive language-image learning.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Reproducible scaling laws for contrastive language-image learning

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This paper cites Gonzalez, Ion Stoica, and Eric P.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Gonzalez, Ion Stoica, and Eric P

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Scaling instruction- finetuned language models

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

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This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Flashattention: Fast and memory-efficient exact attention with io-awareness

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This paper cites Vision transformers need registers.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Vision transformers need registers

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This paper cites Scaling vision transformers to 22 billion pa- rameters.

QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Scaling vision transformers to 22 billion pa- rameters

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Imagenet: A large-scale hierarchical im- age database

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Unveiling encoder-free vision-language models

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation The Llama 3 Herd of Models

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Scal- able pre-training of large autoregressive image models

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Taming transformers for high-resolution image synthesis

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Eva-02: A visual representa- tion for neon genesis

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Dat- acomp: In search of the next generation of multimodal datasets

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Geneval: An object-focused framework for evaluating text- to-image alignment

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Making the v in vqa matter: El- evating the role of image understanding in visual question answering

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Masked autoencoders are scal- able vision learners

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation CLIPScore: A Reference-free Evaluation Metric for Image Captioning

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Gans trained by a two time-scale update rule converge to a local nash equi- librium

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Coincidence, categorization, and consolidation: Learning to recognize sounds with minimal supervision

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Unified language-vision pretraining with dy- namic discrete visual tokenization

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation What matters when building vision-language models?

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Autoregressive image generation using residual quantization

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation DataComp-LM: In search of the next generation of training sets for language models

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Evaluating object hallucination in large vision-language models

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Improved baselines with visual instruction tuning

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Reference 53

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation

Reference 54

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Finite scalar quantization: Vq-vae made simple

Reference 55

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Representation Learning with Contrastive Predictive Coding

Reference 56

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers

Reference 57

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 58

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Learn- ing transferable visual models from natural language super- vision

Reference 59

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 60

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Zero: Memory optimizations toward training trillion parameter models

Reference 61

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Zero-shot text-to-image generation

Reference 62

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation High-resolution image synthesis with latent diffusion models

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Winogrande: An adversarial winograd schema challenge at scale

Reference 64

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation SocialIQA: Commonsense reasoning about social interactions

Reference 65

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation SBER-MoVQGAN or a new effective image encoder for generative models

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Laion coco: 600m synthetic captions from laion2b-en

Reference 67

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Japanese and Ko- rean voice search

Reference 68

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Reference 69

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Reference 70

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

Reference 71

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Reference 72

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Reference 73

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 74

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation EVA-CLIP: Improved Training Techniques for CLIP at Scale

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Rethinking the inception ar- chitecture for computer vision

Reference 76

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 77

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Gemini: A Family of Highly Capable Multimodal Models

Reference 78

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Zerocap: Zero-shot image-to-text generation for visual- semantic arithmetic

Reference 79

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 80

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Reference 81

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 82

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Reference 83

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 84

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Emu3: Next-Token Prediction is All You Need

Reference 85

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Reference 86

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Diffusion models as masked autoencoders

Reference 87

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Reference 88

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Reference 89

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Reference 90

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Reference 91

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Reference 92

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Lan- guage model beats diffusion–tokenizer is key to visual gen- eration

Reference 93

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Reference 94

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Reference 95

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QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation Sigmoid loss for language image pre-training

Reference 96

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Reference 97

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Reference 98

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Reference 99

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Reference 100

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Pith citing papers

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UniCode$^2$: Cascaded Large-scale Codebooks for Unified Multimodal Understanding and Generation cites this paper.

UniCode$^2$: Cascaded Large-scale Codebooks for Unified Multimodal Understanding and Generation QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 84

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MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings cites this paper.

MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 50

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Unified Pix Token And Word Token Generative Language Model cites this paper.

Unified Pix Token And Word Token Generative Language Model QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T21:35:04.591361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 231eadc4-01d1-4ae1-ba6e-263234ac0b94 · inbound

WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens cites this paper.

WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 107

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:08:15.768573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d553d7d-d8bb-4acb-bb92-f667a5f324c6 · inbound

Diffusing in the Right Space: A Systematic Study of Latent Diffusability cites this paper.

Diffusing in the Right Space: A Systematic Study of Latent Diffusability QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 113

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:36:27.631392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b7cfaf30-7abf-4a31-8663-d096ee5faa72 · inbound

NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization cites this paper.

NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T05:27:39.702114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6d93c88c-7d14-4a5d-ae65-d42304fbf072 · inbound

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers cites this paper.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:38:28.819010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d54901a-9046-4a6d-9181-a448ae5835b3 · inbound

Unified Multimodal Autoregressive Modeling with Shared Context-Visual Tokenizer is Key to Unification cites this paper.

Unified Multimodal Autoregressive Modeling with Shared Context-Visual Tokenizer is Key to Unification QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 71

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T20:28:55.821216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aff2e8d0-fbac-4880-9063-09d70c87d728 · inbound

dRAE: Representation Autoencoder with Hyper-Spherical Codes cites this paper.

dRAE: Representation Autoencoder with Hyper-Spherical Codes QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-01T05:45:53.491983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63171561-62da-4f9d-9d63-c7eec9039483 · inbound

Twins: Learn to Predict Unified Representations with Focal Loss cites this paper.

Twins: Learn to Predict Unified Representations with Focal Loss QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:50.752053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc275fa0-7300-4446-9dc3-cc9c0a1d4d35 · inbound

Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes cites this paper.

Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation

Reference 157

Resolution: unresolved; no resolver link, observed 2026-08-06T11:55:28.219048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59f237bf-22fe-4d5c-8e3e-c99693e2e1b9 · inbound

Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes cites this paper.

Reference 157

Resolution: unresolved; no resolver link, observed 2026-08-08T17:08:56.983795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.