activity
20242026
most citedScaling Instructable Agents Across Many Simulated Worlds

9 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CY2026

Work, Wellbeing, and Choice: Empirical Lessons for AI Futures

Stephanie C. Y. Chan, Adam Bales, Katherine L. Hermann +1

Advances in AI-driven automation have raised questions about how humans might find wellbeing in a world where paid employment is less necessary or less available than before. Paid…

cs.AI2026

From AGI to ASI

Tim Genewein, Matija Franklin, Alexander Lerchner +11

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…

cs.LG2025

Strategy Coopetition Explains the Emergence and Transience of In-Context Learning

Aaditya K. Singh, Ted Moskovitz, Sara Dragutinovic +3

In-context learning (ICL) is a powerful ability that emerges in transformer models, enabling them to learn from context without weight updates. Recent work has established emergent…

cs.LG2024★ 2 cited

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Aaditya K. Singh, Ted Moskovitz, Felix Hill +2

In-context learning is a powerful emergent ability in transformer models. Prior work in mechanistic interpretability has identified a circuit element that may be critical for in-co…

cs.RO2024★ 9 cited

Scaling Instructable Agents Across Many Simulated Worlds

SIMA Team, Maria Abi Raad, Arun Ahuja +91

Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires lear…