The paper proposes a new way to describe benchmarks for AI systems that perform knowledge work, outlining four explicit fields: represented activity, tested setting, required work product, and evaluated result. It builds an inventory of 18 work activities from O*NET to enable activity-level reporting across occupations, and evaluates these activities for semantic coherence, algorithm sensitivity, ontology legibility, and human interpretability. The authors apply their framework to three existing benchmarks—GDPval, OfficeQA Pro, and APEX-SWE—to show how different aspects of work can be captured within the same representation.
By Yining Hua, Hongbin Na, Cyrus Ayubcha, Levi Lian
The paper introduces the Agentic Adoption Index (AAI), a new measure of delegated exposure that captures whether workers actually commit tasks to AI within structured workflows. Using semantic embeddings of 888,000 agent skill specifications from GitHub and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those most vulnerable to pre-AI automation, that AAI correlates more with technical capability than with current LLM use, and that for lower‑educated occupations AAI rises with wages while it falls for higher‑educated, high‑earning workers. These patterns also appear in an independent corpus from the Manus Skills Marketplace.
By Hyeongjae Lee, Jihyang Cheon, Lanu Kim
arXiv:2607. 25891v1 Announce Type: new Abstract: Evaluating AI agents in interactive environments is hindered by fragmented tasks, scaffolds, verifiers, and scoring rules.
By Stefan Krsteski, Charlotte Meyer, Guillaume Allegre, Tony O'Halloran, Alexandre Sallinen
The paper introduces a formal framework for measuring AI propensities—tendencies of models to exhibit particular behaviours—using a bilogistic formulation that identifies an "ideal band" of success probability. It estimates the limits of this band with task‑agnostic rubrics and applies the method to six families of LLMs, showing how shifts in propensity affect task performance. The study finds that propensity estimates from one benchmark predict behaviour on held‑out tasks and that combining propensity with capability metrics yields stronger predictive power than either alone.
By Daniel Romero-Alvarado, Fernando Mart\'inez-Plumed, Lorenzo Pacchiardi, Hugo Save, Siddhesh Milind Pawar, Behzad Mehrbakhsh, Pablo Antonio Moreno Casares, Ben Slater, Paolo Bova, Peter Romero, Zachary R. Tidler, Jonathan Prunty, Luning Sun, Jose Hernandez-Orallo
arXiv:2608. 07779v1 Announce Type: new Abstract: Artificial intelligence is changing the task composition of computing work faster than curricula and training typically adapt.
By Majid Memari, George Rudolph
The paper introduces the Agentic Adoption Index (AAI), a new metric that captures whether workers actually delegate tasks to AI within their workflows, rather than merely measuring potential AI applicability. Using 53,000 agent skill specifications and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those previously deemed most at risk, that AAI aligns more closely with AI’s capabilities than current usage, and that adoption peaks at mid‑wage, bachelor’s‑level occupations while declining at both ends of the wage and education spectrum. The study highlights that technical availability explains much of the variation, but other factors—such as resistance to specification or professional discretion—also influence who adopts AI.
whyItMatters":"The findings suggest that actual AI adoption patterns differ from prior risk assessments, indicating that factors beyond technical feasibility shape who delegates to AI, which has implications for workforce planning and policy."
By Hyeongjae Lee, Jihyang Cheon, Lanu Kim