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  "documentTitle": "2025 Air Street Capital The State of AI Report 2025",
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  "notes": "The slide highlights the shift from traditional autoregressive models to diffusion-based models for text generation.",
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      "text": "Recent systems now reach competitive 7-8B quality, add arbitrary-order generation and infilling, and expose useful quality-latency trade-offs.",
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      "text": "Diffusion Modeling in Dream",
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      "text": "LLaDA trains diffusion LMs from scratch using forward masking and reverse denoising with a standard Transformer. It reports competitive general-purpose scores at 8B and extends to a paired vision model (LLaDA-V).\nDream-7B uses arbitrary-order generation and robust infilling with a diffusion decoder. It performs well against similarly sized autoregressive models on reasoning and coding tasks.\nSeed Diffusion targets throughput, reaching ~2,146 tok/s on H20-class GPUs while maintaining competitive code accuracy.\nLongLLaDA analyzes long-context behavior and introduces a training-free length-extension method, showing stable perplexity under extrapolation and a “local perception” effect.",
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