InfoTok: Information-Theoretic Regularization for Capacity-Constrained Shared Visual Tokenization in Unified MLLMs
📰 ArXiv cs.AI
InfoTok introduces information-theoretic regularization for capacity-constrained shared visual tokenization in unified multimodal large language models
Action Steps
- Identify the information-theoretic criteria for shared visual tokenization
- Apply regularization techniques to optimize tokenization for capacity-constrained models
- Evaluate the performance of InfoTok in unified MLLMs using metrics such as token usage efficiency and downstream task accuracy
- Analyze the trade-offs between token budget, model complexity, and performance in InfoTok
Who Needs to Know This
ML researchers and engineers working on multimodal large language models can benefit from InfoTok, as it provides a framework for optimizing shared visual tokenization, which is crucial for efficient and effective multimodal reasoning and synthesis
Key Insight
💡 InfoTok provides a principled approach to shared visual tokenization, enabling more efficient and effective multimodal reasoning and synthesis in unified MLLMs
Share This
💡 InfoTok optimizes shared visual tokenization in unified MLLMs using info-theoretic regularization!
Key Takeaways
InfoTok introduces information-theoretic regularization for capacity-constrained shared visual tokenization in unified multimodal large language models
Full Article
Title: InfoTok: Information-Theoretic Regularization for Capacity-Constrained Shared Visual Tokenization in Unified MLLMs
Abstract:
arXiv:2602.01554v2 Announce Type: replace-cross Abstract: Unified multimodal large language models (MLLMs) aim to unify image understanding and image generation within a single framework, where a shared visual tokenizer serves as the sole interface that maps high-dimensional images into a limited token budget for downstream multimodal reasoning and synthesis. However, existing shared-token designs are largely architecture-driven and lack an explicit criterion for what information should be prese
Abstract:
arXiv:2602.01554v2 Announce Type: replace-cross Abstract: Unified multimodal large language models (MLLMs) aim to unify image understanding and image generation within a single framework, where a shared visual tokenizer serves as the sole interface that maps high-dimensional images into a limited token budget for downstream multimodal reasoning and synthesis. However, existing shared-token designs are largely architecture-driven and lack an explicit criterion for what information should be prese
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