AI for science: a decade of forecast gains, and math checked at scale
Summary
Two releases show AI research paying off outside language-model leaderboards. DeepMind's WeatherNext Cyclones adds more than a full extra day of useful cyclone-forecast lead time -- three-day forecasts now as accurate as prior models' two-day -- from input data at 28x28km resolution, 100x coarser than traditional physics models. The team calls the jump roughly a decade of normal meteorological progress. Separately, Mistral's Leanstral 1.5, a 119B / 6B-active open-weights Lean 4 proof model, saturates miniF2F, solves 587 of 672 PutnamBench problems, and found 5 previously unreported bugs across 57 real open-source repos via machine-checked proof search.
View data as table
| Prior models | 2 | two-day forecast at this accuracy |
|---|---|---|
| WeatherNext Cyclones | 3 | three-day forecast at the same accuracy |
Formal math leaves the benchmark and finds real bugs
Mistral's Leanstral 1.5 is a 119B-parameter Mixture-of-Experts model with 6B active per token, open-weights under Apache-2.0, targeting Lean 4. It saturates miniF2F (100% on validation and test) and solves 587 of 672 PutnamBench problems. The tell that formal verification is now a practitioner tool, not a trophy: run as machine-checked proof search over open-source code, it surfaced 5 previously unreported bugs across 57 real repositories.
View data as table
| miniF2F (test) | 100% | saturated, 100% |
|---|---|---|
| PutnamBench | 87.4% | 587 of 672 problems |
- A decade of forecasting progress at once. WeatherNext Cyclones makes three-day cyclone forecasts as good as prior models' two-day -- more than a full 24 hours of extra lead time, which DeepMind frames as roughly a decade of normal meteorological progress.
- Coarse data, sharper answers. The model needs input at only 28x28km resolution, 100x coarser than traditional physics models, and ran a 1,000-member ensemble that helped the National Hurricane Center forecast Hurricane Melissa's rapid intensification and Jamaica landfall.
- Open-weights formal math at scale. Leanstral 1.5 is a 119B / 6B-active MoE under Apache-2.0 that saturates miniF2F (100%) and solves 587 of 672 PutnamBench problems -- an 87% solve rate, every proof machine-checked in Lean 4.
- Proof search as a bug finder. Pointed at real code, Leanstral 1.5 uncovered 5 previously unreported bugs across 57 open-source repositories -- formal verification moving from leaderboard to practitioner tool, output that is checkable rather than merely benchmarked.
Digest window 2026-W33. Both items' facts trace to the developing lab's own release (DeepMind, Mistral), not to news write-ups; trade press is cited as context only. These are lab-self-reported results, not independent peer review -- WeatherNext's forecast-skill claims and Leanstral's benchmark scores are the developers' own. Leanstral's Lean 4 proofs are, however, machine-checked by the Lean kernel, and the 5 repository bugs are the concrete, checkable output. Distinct from prior cycles' reasoning-benchmark and training-efficiency digests: this batch is AI producing verifiable scientific and mathematical output, not better leaderboard scores.
Sources(3) ▾
- Google DeepMind, WeatherNext AI model achieves breakthrough in forecasting cyclones (2026-07) — Google DeepMind's own release announcing WeatherNext Cyclones. Load-bearing source for the lead-time gain (three-day forecasts as good as prior models' two-day, more than 24 hours of extra lead time), the 'roughly a decade's worth of meteorological progress' framing, the 28x28km training resolution (100x coarser than traditional physics models), the 1,000-member ensemble, the ~20 TB / IBTrACS ~5,000-storm training data, and the Hurricane Melissa / National Hurricane Center collaboration. Fetched directly (HTTP 200). deepmind.google
- Mistral AI, Leanstral 1.5 (2026-07) — Mistral AI's own release for Leanstral 1.5. Load-bearing source for the 119B-total / 6B-active parameter counts, the Apache-2.0 open-weights license, the Lean 4 target, the miniF2F saturation (100% validation and test), the 587/672 PutnamBench result, and the discovery of 5 previously unreported bugs across 57 tested open-source repositories via machine-checked proof search. Fetched directly (HTTP 200). mistral.ai
- MarkTechPost, Mistral AI Releases Leanstral 1.5: An Apache-2.0 Lean 4 Code Agent Model Solving 587 of 672 PutnamBench Problems (2026-07-03) — Trade-press write-up of the Leanstral 1.5 release. Corroboration and context only; every figure in this digest traces to Mistral's own release, not to this page. Fetched directly (HTTP 200). marktechpost.com
Comments
Always open. Logged-in readers can annotate paragraphs in place.
AI research is starting to pay off outside language-model leaderboards. DeepMind's WeatherNext Cyclones adds more than a full day of useful cyclone-forecast lead time -- its three-day forecasts are as accurate as prior models' two-day -- a jump the team calls roughly a decade's worth of meteorological progress. It does this from input data at just 28x28km resolution, 100x coarser than traditional physics models.