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METR · August 6, 2024

Agent benchmarks: inspect task weighting and budget-dependent failure

guide Model EvaluationAI Engineering
Why it matters

METR’s August 2024 evaluation update describes expanding its task set, using human completion time to organize difficulty and weighting task families to limit domination by families with many variants. It examines how success changes with additional agent time and where progress levels off under a simple harness. Sparse human baselines and the mixture of easy and hard tasks limit interpretation. The reusable lesson is that an aggregate score depends on task composition, weighting, stopping behavior and scaffolding, not just the model.

My takeaway: Report success by difficulty bucket and task family alongside the aggregate. Document weights, human-baseline coverage and stopping causes, then plot success against the allowed budget before concluding that more agent runtime improves capability.
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