VisiveAI

Lots of debate this week on whether journals should ban AI in peer review. We actually tested this in our new preprint, and the results were pretty clear to me. For context, I've reviewed for most of the big journals and evaluated 1,000 human reviews as an editor. I love to read and evaluate science. TLDR: From now on I won't do any peer review without AI. For clinical trials, I'd even call human-only review dangerous 🧵

I've always believed that intelligence should come from simple, general building blocks that learn their own abstractions directly from raw data; in other words, the bitter lesson. I think we're finally seeing a real step towards AI that can learn from all data. Language modeling on raw bytes is a microcosm of this ideal, and to me it's a central problem in AI: if we can't build LLMs that learn end-to-end from bytes, how will we handle the rest of the world's data that we understand even less? Last year we introduced hierarchical networks (H-Nets) as a first step. Our new version finds up to 3-5x efficiency gains in controlled scaling law experiments, and at larger scale matches an open-source model trained on 11T tokens with ~10x less data and compute. The posts below walk through what we found and why we think it works.

New image model coming up from @OpenAI: "GPT-Image-2.6" This should be rolling out soon.

"Contextual Correction of Parallel Drafts for Speculative Decoding" Parallel speculative decoding can generate multiple tokens at once, but the tokens can be inconsistent because they don't know which earlier tokens were selected. This paper DCOCO uses a lightweight corrector to score token combinations in parallel, improving draft accuracy without the expensive sequential computation. Across four LLMs, it achieves up to 6.6x faster decoding than standard generation and 8-19% higher throughput than competing speculative decoding methods. alphaxiv.org/abs/2610.12570

introducing prism, the world’s first speech-to-speech model you can’t tell from a human in conversation prism has: > 300ms response time, 4x faster than OpenAI GPT-Live > outperforms every other s2s model on intelligence (with NO reasoning) > the first s2s model customizable through reinforcement learning we’re a team of 35 with $35M in funding, and we’re unlocking s2s model adoption faster than openai, google, and x. the first 100 people to sign up here get 1 month free: rime.ai/prism?utm_campaign=p…