mlsanityData to 7 Oct 2026

Unified Multimodal Understanding via Byte-Pair Visual Encoding

arXiv 2506.23639 ↗First posted 30 Jun 2025cs.CV cs.AIcited by 9

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

Multimodal large language models (MLLMs) have made significant progress in vision-language understanding, yet effectively aligning different modalities remains a fundamental challenge. We present a framework that unifies multimodal understanding by applying byte-pair encoding to visual tokens. Unlike conventional approaches that rely on modality-specific encoders, our method directly incorporates structural information into visual tokens, mirroring successful tokenization strategies in text-only language models. We introduce a priority-guided encoding scheme that considers both frequency and spatial consistency, coupled with a multi-stage training procedure based on curriculum-driven data composition. These enhancements enable the transformer model to better capture cross-modal relationships and reason with visual information. Comprehensive experiments demonstrate improved performance across diverse vision-language tasks. By bridging the gap between visual and textual representations, our approach contributes to the advancement of more capable and efficient multimodal foundation models.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 57 of its 72 references that are in the map. First posted on arXiv 30 Jun 2025.

Builds on

57 of 72 references.

Closest ancestors · 15

Same direction, newest first

  1. 2024DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding13 Dec
  2. 2024Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data24 Oct
  3. 2024A Survey on Multimodal Benchmarks: In the Era of Large AI Models21 Sep
  4. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution18 Sep
  5. 2024A Survey on Benchmarks of Multimodal Large Language Models16 Aug
  6. 2024LLaVA-OneVision: Easy Visual Task Transfer6 Aug
  7. 2024UniCode: Learning a Unified Codebook for Multimodal Large Language Models14 Mar
  8. 2024Can MLLMs Perform Text-to-Image In-Context Learning?2 Feb
  9. 2023ShareGPT4V: Improving Large Multi-Modal Models with Better Captions21 Nov
  10. 2023mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration7 Nov
  11. 2023Improved Baselines with Visual Instruction Tuning5 Oct
  12. 2023MMBench: Is Your Multi-modal Model an All-around Player?12 Jul
3 more
  1. 2023A Survey on Multimodal Large Language Models23 Jun
  2. 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models23 Jun
  3. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning11 May

Other references · 42

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2024The Llama 3 Herd of Modelscited by 14,192
  4. 2023Visual Instruction Tuningcited by 7,792
  5. 2025Qwen2.5-VL Technical Reportcited by 5,418
  6. 2017Neural Discrete Representation Learningcited by 4,650
  7. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  8. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  9. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  10. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,048
  11. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  12. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,498
30 more
  1. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,179
  2. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,881
  3. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,696
  4. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,376
  5. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
  6. 2024Chameleon: Mixed-Modal Early-Fusion Foundation Modelscited by 904
  7. 2018VizWiz Grand Challenge: Answering Visual Questions from Blind Peoplecited by 852
  8. 2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 776
  9. 2024Emu3: Next-Token Prediction is All You Needcited by 761
  10. 2022A-OKVQA: A Benchmark for Visual Question Answering using World Knowledgecited by 702
  11. 2023LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Modelcited by 602
  12. 2022Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Taskscited by 420
  13. 2024Hallucination of Multimodal Large Language Models: A Surveycited by 380
  14. 2023Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Actioncited by 301
  15. 2021ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Biascited by 216
  16. 2025EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agentscited by 213
  17. 2024ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Modelscited by 204
  18. 2024Vision language models are blind: Failing to translate detailed visual features into wordscited by 144
  19. 2022ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyondcited by 117
  20. 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 83
  21. 2024Language Models Still Struggle to Zero-shot Reason about Time Seriescited by 81
  22. 2024Visual Hallucinations of Multi-modal Large Language Modelscited by 68
  23. 2023Vision Transformer with Quadrangle Attentioncited by 46
  24. 2024Attention with Markov: A Framework for Principled Analysis of Transformers via Markov Chainscited by 39
  25. 2024Toward a Theory of Tokenization in LLMscited by 28
  26. 2024From Pixels to Tokens: Byte-Pair Encoding on Quantized Visual Modalitiescited by 16
  27. 2024Theoretical Analysis of Byte-Pair Encodingcited by 15
  28. 2024Analyzing The Language of Visual Tokenscited by 12
  29. 2024VideoOrion: Tokenizing Object Dynamics in Videoscited by 12
  30. 2024Exploring Selective Layer Fine-Tuning in Federated Learningcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 6.

9 papers.

  1. 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 195
  2. 2025Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videoscited by 94
  3. 2026Being-H0.7: A Latent World-Action Model from Egocentric Videoscited by 68
  4. 2026Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalizationcited by 61
  5. 2026ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Modelcited by 12
  6. 2026Rethinking Visual-Language-Action Model Scaling: Alignment, Mixture, and Regularizationcited by 7
  7. 2025Transport Discrepancy as a Reliability Signal for Vision-Language-Action Modelscited by 6
  8. 2026BackdoorIDS: Zero-shot Backdoor Detection for Pretrained Vision Encodercited by 1
  9. 2026FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations
  2. 2025Perceiving Beyond Language Priors: Enhancing Visual Comprehension and Attention in Multimodal Models
  3. 2026What Do Visual Tokens Really Encode? Uncovering Sparsity and Redundancy in Multimodal Large Language Models
  4. 2025Visual Representation Alignment for Multimodal Large Language Models
  5. 2025UniCode2: Cascaded Large-scale Codebooks for Unified Multimodal Understanding and Generation
  6. 2023LION : Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge
  7. 2025Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual Attention for Multimodal LLMs
  8. 2025Token Sequence Compression for Efficient Multimodal Computing