Course promo
A Self-Correcting Diffusion Agent Model From Inclusion AI
Inclusion AI, part of Ant Group, has released an open-weight diffusion model aimed at agent workloads: instead of predicting one token at a time left to right, it drafts a whole block of text and refines it over successive passes. Two design choices matter for agents — Levenshtein-style editing gives it explicit delete and insert moves so it can rip out a wrong chunk and slot in a correction mid-generation rather than plowing forward with the mistake baked in, and block routing keeps its mixture-of-experts selection efficient over long contexts. The video cites roughly 703 tokens/second on the BFCL tool-use benchmark with a score around 61, close to double the throughput of a comparable model on the same work, with weights on Hugging Face and GitHub. It's around 100B parameters, so it needs real GPU capacity or a cloud host — not a laptop.
First, the video is largely a promotion for the creator's paid community, with the model used as the hook — we've summarised the technical claims and dropped the rest. Second, the auto-generated captions render the model's name inconsistently (as both "Ladder 2.2-Flash" and "Llama 2.2-Flash"), so we've deliberately not stated a product name we can't verify. Check Inclusion AI's Hugging Face and GitHub listings for the actual release name and benchmark figures before relying on any of these numbers.
Watch on YouTube → · Compare agent model benchmarks → · ← Back to News
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