Tagged long-horizon tasks · show all articles
A long agent loop resends its own history on every step, pays a fixed tool tax each time, and risks a retry that resends it twice. That arithmetic, not model quality, sets the real ceiling on how far a trajectory can run.
A single successful run shows a system can succeed once. Pass@k, pass^k, and repeated-trial statistics are how anyone finds out whether it succeeds reliably — and task length changes the answer.
Long tasks fail through state corruption, weak observations, compounding decisions, and false completion—not merely because the model writes a bad line of code.