Agents' Last Exam
Quick Answer
This paper shows that The Agents' Last Exam (ALE) benchmark evaluates AI agents on economically valuable tasks, revealing a mere 2.6% average pass rate across configurations.
Quick Take
The Agents' Last Exam (ALE) benchmark evaluates AI agents on economically valuable tasks, revealing a mere 2.6% average pass rate across configurations. Developed with input from over 250 industry experts, ALE aims to bridge the gap between AI benchmark success and real-world economic impact.
Key Points
- ALE focuses on long-horizon tasks with verifiable outcomes in non-physical industries.
- The benchmark includes over 1,000 tasks organized into 55 subfields across 13 industry clusters.
- Current AI models show a low average pass rate of 2.6% on ALE's hardest tier.
- ALE is designed to evolve continuously with new workflows and industries.
- The initiative aims to connect AI performance with GDP-relevant economic contributions.
Article Content
From source RSS / original summaryarXiv:2606. 05405v1 Announce Type: new Abstract: Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows.
This paper introduces Agents' Last Exam (ALE), a benchmark designed to evaluate AI agents on long-horizon, economically valuable, real-world tasks with verifiable outcomes. Developed in collaboration with 250+ industry experts, ALE covers non-physical industries defined with reference to O*NET / SOC 2018 (the U. S. federal occupational taxonomy). It is organized around a task taxonomy with 55 subfields grouped into 13 industry clusters covering 1K+ tasks.
Current results show that the hardest tier remains far from saturated: across mainstream harness and backbone configurations, the average full pass rate is 2. 6%. ALE is designed as a living benchmark: its task pool grows continuously as new workflows and industries are onboarded. More broadly, ALE is intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP-relevant impact.
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