Who's Who of AI

Abdoulaye Diack

111 trust practitioner @diack.bsky.social · 1,933 followers
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Tracked through public AI activity and peer connections inside the directory.

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Program Manager ML & AI @ Google Research | Ex-Google Brain. Speaker (FR/EN) Abdoulaye.ai ⠦ Thinking... 🌍 Accra, Ghana (cover photo: Dar Es Saalam, circa 2015)
What they're sharing

Articles & links

This week, I bumped into Hermann at Stanford, a PhD student from Cameroon 🇨🇲, sharing his brillant paper here! Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation arxiv.org/abs/2610.14162

Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation arxiv.org
AI Weekly's analysis →
  • Next-Concept Modeling encodes text with a multiscale residual VQ-VAE, then a transformer predicts all codes within each level in parallel.
  • On LM1B, NCM reports better generative perplexity than the strongest diffusion LM baseline while running 21× faster at matched parameter scales.
  • On XSum, NCM matches the strongest DLM's ROUGE-L at a 31× speedup; on OpenWebText it is competitive at 10× faster.
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View on Bluesky · ♥ 30 ↻ 2 ↩ 0 · 3 from the directory shared this · 1d ago
↻ Abdoulaye Diack reposted
@unsloth.ai

Google releases DiffusionGemma.✨ The new 26B-A4B diffusion text model runs locally on 18GB RAM. It supports high-speed text generation, thinking, image, video and 256K context. Run and train via Unsloth Studio. GGUF: huggingface.co/unsloth/diff... Guide: unsloth.ai/docs/models…

DiffusionGemma - How to Run Locally | Unsloth Documentation unsloth.ai
AI Weekly's analysis →
  • DiffusionGemma 26B-A4B generates text in parallel using diffusion refinement rather than token-by-token decoding.
  • The 4-bit quantized model requires only 18 GB RAM and reaches 2,000+ tokens per second on an RTX 6000.
  • Speed comes at a cost: AIME 2026 accuracy drops from 88.3% on standard Gemma 4 to 69.1% on DiffusionGemma.
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