What predicts success in ultra-long-horizon agent tasks?

AUTOLAB reframes what a long-horizon agent benchmark should test. Most agentic evals score either single-turn responses or short interactive trajectories; AUTOLAB instead hands the agent a correct but deliberately suboptimal baseline across 36 expert-curated tasks (system optimization, CUDA kernels, model development, puzzles) and asks it to improve the artifact within a strict wall-clock budget. The striking empirical result, across 17 frontier models, is that the dominant predictor of success is not the quality of the agent's initial attempt but its persistence — its willingness to repeatedly benchmark, edit, and incorporate noisy empirical feedback over many cycles. Most models, including proprietary ones, either terminate prematurely or exhaust their budget with minimal progress; claude-opus-4.6 is called out as a strong exception.

This is a sharper, more operational claim than "agents should iterate." It says the binding constraint is a behavioral disposition toward sustained empirical grounding, and that disposition is unevenly distributed across models that look comparable on one-shot benchmarks. It grounds Should agent evaluation measure more than task success? with a concrete trajectory-level predictor, and it sits naturally alongside Does raw token spending actually predict agent performance? — persistence only pays if each loop returns informative, retained feedback, otherwise it is budget-burning churn, not progress.

The mechanism cuts against itself, however. Do models fail worse when their own errors fill the context? implies that more loops mean more accumulated mistakes in context, which should degrade the very iteration AUTOLAB rewards. The reconciliation is probably that persistence pays only when paired with calibrated scoring that lets the agent see whether an edit actually helped — pure persistence without trustworthy feedback would amplify error. That is why the authors single out harness design as the promising lever: the harness, not the backbone alone, decides whether long horizons compound feedback or compound noise.