Google Gemma(@googlegemma):"DiffusionGemma as Jev" showcases the power of non-autoregressive architectures. While Jev demonstrates the value of rapid decision models, running DiffusionGemma in this paradigm leverages canvas diffusion to evaluate structured choices in a single parallel pass: ⚡ ️Massive Parallelism: Denoises across an open canvas in a single step instead of sequential autoregressive token generation (~0.2s on a DGX spark). 🧠 Full Bidirectional Attention: Allows every option to attend to the full context concurrently, yielding well-calibrated decision distributions. 👁️ Multimodal Grounding: Inherits Gemma 4's spatial vision capabilities for complex visual and text decisions. Read more about this approach here: https://t.co/hCEg276mzA https://t.co/vRLhy6KECT

2026.09.18 22:04

"DiffusionGemma as Jev" showcases the power of non-autoregressive architectures. While Jev demonstrates the value of rapid decision models, running DiffusionGemma in this paradigm leverages canvas diffusion to evaluate structured choices in a single parallel pass: ⚡ ️Massive Parallelism: Denoises across an open canvas in a single step instead of sequential autoregressive token generation (~0.2s on a DGX spark). 🧠 Full Bidirectional Attention: Allows every option to attend to the full context concurrently, yielding well-calibrated decision distributions. 👁️ Multimodal Grounding: Inherits Gemma 4's spatial vision capabilities for complex visual and text decisions. Read more about this approach here:

Matt Mastracci@mmastrac

2026.09.17 16:41

I ran some real, live evals on Jev vs DiffusionGemma-as-Jev (my patch for vLLM!) DiffusionGemma comes out as the winner, I think. Headlines: Is Jev faster than DiffusionGemma? No ❌ (API vs DGX Spark) Is Jev smarter than DiffusionGemma? No ❌ (they're roughly tied!)