Task subset library
Browse approaches in a table. Preview the selected tasks, then export CSV or JSON. Seed 0 is used for randomized methods.
Choose & download a subset ↗Every task costs time. Make the ones you evaluate count.
We study how to choose benchmark tasks for two different goals: estimating an agent’s success rate and discovering its failures earlier.
The strategy sets the order. Your budget sets where to stop. This illustration does not show measured savings.
The best strategies for each objective, at your selected task budget. Winners are selected by median performance in the Sonnet mixed-rubric results.
Costs are median shares of recorded full-suite execution cost; ordering and judging costs are excluded. Performance values are medians across agents, after averaging randomized runs within each agent. Failure uplift is a ratio of these medians, not a mean per-agent improvement. A partial evaluation does not provide full-suite coverage. Winners are retrospective; they are not guaranteed to win on a new benchmark.
Explore the full cost and results tables ↗Choose any of the 15 approaches, select a 10%, 20%, or 50% budget, and download ordered task IDs with configuration metadata.
Browse approaches in a table. Preview the selected tasks, then export CSV or JSON. Seed 0 is used for randomized methods.
Choose & download a subset ↗Explore the agents in each benchmark. Compare rankings at each budget with the full-suite ranking.
Explore agents & rankings ↗Includes all ten benchmark settings. Cost comparisons above cover only Verified and Multilingual. Historical Mid-Range downloads are target-specific; the library asks which target agent you want to evaluate.
Finding failures and estimating overall performance are different evaluation needs. A useful selection strategy depends on which question you want to answer.
Estimate the success rate of the complete benchmark from a smaller subset of tasks.
Explore how black-box strategies compare with difficulty-based selection and Random.
Compare estimation errors ↗Prioritize tasks that reveal failures early, when only part of the benchmark can be evaluated.
Explore how task-difficulty signals, including LLM judges, change which failures appear first.
Compare failure detection ↗Tasks are prioritized before evaluating the target agent. The selected prefix determines the evaluation budget.
Use information available before execution: task text, metadata, or estimated difficulty.
Compare baselines, adapted black-box regression techniques, and task-side difficulty estimation.
Measure success-rate estimation error and failures found at 10%, 20%, and 50% task budgets.
Ten benchmark settings span coding, terminal use, tool interactions, research, and web-based tasks.
Benchmark settings are not all independent task suites: SWE-bench Mini overlaps with SWE-bench Verified.
Compare strategies, change the budget, and inspect results across benchmarks and judge configurations.
Open the research dashboard ↗