activity
20242026
most citedWhat needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

2 citations · 2 across the 3 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.LG20242 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

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…