arXiv AI

Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting

arXiv:2607. 02467v1 Announce Type: cross Abstract: Whether pairing people with AI helps or hurts is usually reported as a single average effect.

arXiv AI
Jun 16

AI systems out-persuade expert humans

arXiv:2606. 16475v1 Announce Type: cross Abstract: Many societal decisions are settled by contests of persuasion.

By Kobi Hackenburg, Caroline Wagner, Luke Hewitt, Ben M. Tappin, Ed Saunders, Hannah Rose Kirk, Helen Margetts, Christopher Summerfield
arXiv AI
Aug 11

The Scaling Paradox in Human-AI Collaboration

arXiv:2608. 00818v2 Announce Type: replace Abstract: The discovery of scaling laws has highlighted the extraordinary potential of AI systems with a striking empirical pattern: as AI systems scale, their capabilities tend to improve predictably.

By Anyan Qi, Mengxin Wang
arXiv AI
2d ago

When the AI Leaves the Tailorshop: Measuring What an LLM Advisor Leaves Behind in Complex Problem Solving

The study investigates how large language model (LLM) advisors affect complex problem‑solving in a simulated clothing‑factory setting. Two preregistered experiments (N=200 and N=198) found that participants with AI support reported higher confidence and understanding, expended less effort, and in some cases achieved better performance or avoided bankruptcy. Within the AI‑supported group, more frequent changes to the AI’s recommendations were linked to improved unaided performance and knowledge.

By Robin Welsch
arXiv AI
Jun 16

Artificial Intelligence Index Report 2026

arXiv:2606. 15708v1 Announce Type: new Abstract: Welcome to the ninth edition of the AI Index report.

By Sha Sajadieh, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Lapo Santarlasci, Juan Pava, Nestor Maslej, Russ Altman, Erik Brynjolfsson, Carla Brodley, Jack Clark, Virginia Dignum, Vipin Kumar, James Landay, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Elham Tabassi, Russell Wald, Toby Walsh, Dan Weld
arXiv AI
Aug 17

AI Evaluation Should Work With Humans

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.

By Jan Kulveit, Gavin Leech, Tom\'a\v{s} Gaven\v{c}iak, Raymond Douglas