9 citations · 15 across the 6 of their papers we have counts for
6 papers
SIMA 2: A Generalist Embodied Agent for Virtual Worlds
SIMA team, Adrian Bolton, Alexander Lerchner +63
We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a signifi…
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…
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…
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…
The Transient Nature of Emergent In-Context Learning in Transformers
Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz +3
Transformer neural networks can exhibit a surprising capacity for in-context learning (ICL) despite not being explicitly trained for it. Prior work has provided a deeper understand…
SODA: Bottleneck Diffusion Models for Representation Learning
Drew A. Hudson, Daniel Zoran, Mateusz Malinowski +6
We introduce SODA, a self-supervised diffusion model, designed for representation learning. The model incorporates an image encoder, which distills a source view into a compact rep…