arXiv:2608. 13577v1 Announce Type: new Abstract: This position paper argues that the dominant paradigm of AI evaluation (which focuses on superhuman autonomous performance and so implicitly targets the goal of replacing humans) is guiding AI development in the wrong direction.
arXiv:2502. 20502v2 Announce Type: replace Abstract: Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition.
arXiv:2606. 15708v1 Announce Type: new Abstract: Welcome to the ninth edition of the AI Index report.
arXiv:2606. 24854v1 Announce Type: cross Abstract: Artificial intelligence (AI) can enhance what people who use augmentative and alternative communication (AAC) are able to do with their systems.
Scaling human oversight of AI systems for tasks that are difficult to evaluate.
Game Arena is a new, open-source platform for rigorous evaluation of AI models. It allows for head-to-head comparison of frontier systems in environments with clear winning conditions.
The paper introduces Atria Dawn Preview, a foundation agentic language model aimed at scientific research and engineering workflows. Trained through a Verifiable Experience Pipeline, it performs competitively across 16 real‑world benchmarks, achieving the highest scores on five. The authors also present a detailed case study of human–AI collaboration, showing that while AI proposes methods and revisions, humans retain final decision‑making and guide the research direction.
arXiv:2607. 21547v1 Announce Type: new Abstract: The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible.
arXiv:2608. 02996v1 Announce Type: new Abstract: Latin America is missing a foundational layer for native AI development: the benchmark layer.
The paper demonstrates that humans and AI systems achieve better performance when collaborating rather than working alone. It investigates how two design dimensions—autonomy and initiative—shape collaboration patterns, using a paradox perspective to uncover internal tensions and map underlying paradoxes. From this analysis, the authors derive four distinct human‑AI collaboration patterns: Instruction, Delegation, Assistance, and Co‑creation.
arXiv:2606. 19899v1 Announce Type: cross Abstract: This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evidence about the biological capabilities and risks of AI scientists, or agentic AI systems capable of autonomously or collaboratively performing multi-step scientific tasks.
arXiv:2601. 09753v2 Announce Type: replace-cross Abstract: AI science evaluation tools aim to assess research credibility.
arXiv:2606. 12683v1 Announce Type: new Abstract: Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations.
