Revisiting MLLM Token Technology through the Lens of Classical Visual Coding

2025-08-18

AI Summary

Multimodal large language models (MLLMs) suffer from suboptimal token efficiency and robustness due to ad hoc tokenization strategies lacking principled foundations. Method: This work establishes, for the first time, a systematic mapping between MLLM tokenization, token compression, and token-level reasoning mechanisms and classical visual coding principles—including transform coding, rate-distortion optimization, and hierarchical representation—thereby constructing a unified cross-domain analytical framework. We propose a “bidirectional inspiration” mechanism: leveraging visual coding theory to guide MLLM token design while using MLLM semantic modeling to inform innovations in semantic visual encoders/decoders. Joint optimization of information fidelity and computational cost enables modular comparative analysis. Contribution/Results: The framework provides an interpretable theoretical foundation and a novel paradigm for efficient multimodal model compression, lightweight deployment, and the design of next-generation semantic visual codecs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Classical visual coding and Multimodal Large Language Model (MLLM) token technology share the core objective - maximizing information fidelity while minimizing computational cost. Therefore, this paper reexamines MLLM token technology, including tokenization, token compression, and token reasoning, through the established principles of long-developed visual coding area. From this perspective, we (1) establish a unified formulation bridging token technology and visual coding, enabling a systematic, module-by-module comparative analysis; (2) synthesize bidirectional insights, exploring how visual coding principles can enhance MLLM token techniques' efficiency and robustness, and conversely, how token technology paradigms can inform the design of next-generation semantic visual codecs; (3) prospect for promising future research directions and critical unsolved challenges. In summary, this study presents the first comprehensive and structured technology comparison of MLLM token and visual coding, paving the way for more efficient multimodal models and more powerful visual codecs simultaneously.

Problem

• Bridging MLLM token technology with classical visual coding principles
• Enhancing MLLM token efficiency and robustness using visual coding insights
• Developing next-generation semantic visual codecs through token technology paradigms

Innovation

• Unified formulation bridging token and visual coding
• Bidirectional insights enhancing efficiency and robustness
• Systematic module-by-module comparative analysis framework