Layna Paraboschi

Yiyou Sun, Xinyang Han, Weichen Zhang et al.

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.

Shravika Mittal, Erin Kasson, Layna Paraboschi et al. · gatech

Generative AI (GenAI) is increasingly being integrated into the online ecosystem, including online health communities (OHCs), where people with diverse health conditions exchange social support. For example, in OHCs, support providers are beginning to share content generated, directly or indirectly, by popular GenAI-based tools. OHCs are governed by norms that define appropriate behavior when providing support. Ways in which AI-generated support interacts with these norms remain underexplored. Inappropriate conformance or outright violation can erode seekers' trust, distort decision-making, and threaten community sustenance. In this work, we examine whether (and how) AI-generated support conforms to norms, using popular opioid-use recovery subreddits as our testbed. First, we provide an inventory of norms regulating text-based support provision in OHCs. Next, using human-validated LLM judges, we assess the prevalence of AI's conformity to these norms. Finally, through an expert review, we identify risks to seekers (and OHCs) resulting from norm (non)conformity. Our analysis revealed that, while AI-generated support conforms to norms, such conformity may be inappropriate or insufficient, for example, by over- or under-validating seekers in distress. Moreover, we observed instances of outright norm violation. This work provides insights that can help moderators and OHC designers adapt existing and develop new norms to regulate AI integration, protecting both seekers and communities they rely on.