AI RESEARCH
CrystaL: Spontaneous Emergence of Visual Latents in MLLMs
arXiv CS.CV
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ArXi:2602.20980v2 Announce Type: replace Multimodal Large Language Models (MLLMs) have achieved remarkable performance by integrating powerful language backbones with large-scale visual encoders. Among these, latent Chain-of-Thought (CoT) methods enable implicit reasoning in continuous hidden states, facilitating seamless vision-language integration and faster inference. However, existing heuristically predefined supervision signals in latent CoT provide limited guidance for preserving critical visual information in intermediate latent states.