3步推理生成加速20+倍！CoLT教会多模态大模型用「潜思维链」思考

TL;DR — Based only on the title, CoLT is a method that teaches multimodal large models to reason via a "latent chain-of-thought," reportedly speeding up reasoning/generation by 20+ times through a 3-step process. It matters because it targets faster, more efficient reasoning in vision-language/multimodal systems.

• Introduces "CoLT," which appears to enable multimodal LLMs to think using a latent (non-verbalized) chain-of-thought rather than explicit token-by-token reasoning.
• Claims a 20×+ acceleration in reasoning/generation, achieved via a described 3-step process.
• Positioned for multimodal ("多模态") models, suggesting cross-modal reasoning tasks.
• Note: content is thin (title only), so specifics on architecture, benchmarks, and how the "3 steps" and latent CoT work are unverified and inferred from the headline.