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Research2026-03-22 · source-backed
Think in Diffusion, Talk in Autoregression: drafts tokens in parallelized diffusion then outputs autoregressively in a single forward pass. The draft model is the base model itself. First architecture to close the quality gap with pure AR at nearly 6x throughput. Source
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Two papers land the same week: DreamReasoner-8B uses block-size curriculum learning to scale parallel block-wise denoising for long chain-of-thought (arXiv 2606.19257), and Diffusion-Proof applies diffusion-style generation to formal theorem proving (arXiv 2606.19315). The cas...
Co-led by Lightspeed and Diffusion with Sapphire and Whale Rock joining, up from $11B in March, on ARR past $350-400M with 80% of Am Law 100 firms as customers. Capital is earmarked for proprietary models rather than wrapping frontier ones. Bloomberg A vertical app company buy...
The PlayCoder benchmark is the cold shower the vibe coding movement needed. Researchers tested 10 state-of-the-art code LLMs on generating GUI applications across six categories. The models achieved high compilation rates. The code built and ran. But when they measured whether...
Amid a week of pricing and commerce stories, here's hard tech you can actually download. Google released DiffusionGemma on June 10, a 26B-parameter Mixture-of-Experts model (3.8B active) that generates text by diffusion instead of left-to-right decoding. The architecture is th...
No separate draft model, no expensive verification trees, code released (arXiv). dLLMs like MDLM and SEDD have been an interesting-but-slow alternative to autoregressive generation. This makes them viable for latency-sensitive work, which is the gate they've been stuck behind.
Nolan Lawson (ex-Microsoft, ex-Salesforce) published an essay that hit 662 points and 247 comments on Hacker News. His argument: stop using LLMs to ship faster. Use them to ship better. His approach runs multiple models to review code, ranks findings by criticality, and filter...
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