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A. Chen, M. Patel & collaborators
What happens when a model encounters a problem it has never seen? A closer look at compositional reasoning, and the gap between remembering an answer and finding one.
Explores how reasoning emerges from smaller, reusable skills.
L. Rivera & S. Park
Comparing representations across brains and models reveals surprising similarities in how both organize visual information.
E. Morgan, J. Liu & collaborators
A data-driven approach to understanding ocean temperature variability across timescales, from seasonal cycles to long-term shifts.
R. Shah & T. Wilson
Rethinking the relationship between model size and planning. This study explores how structured memory can help compact models tackle longer tasks.
A different perspective on scaling: better memory, rather than more parameters.
K. James & collaborators
New perspectives on the interplay between attention, novelty, and memory consolidation.
D. Kim, A. Singh & collaborators
Scientific models should know when they might be wrong. An exploration of uncertainty-aware learning for choosing more informative experiments.
N. Brooks & M. Costa
Connecting global simulations with regional observations to better understand extreme weather. A framework for preserving local detail without losing the bigger picture.
Bridges physical simulation and machine learning.
S. Rao & collaborators
Following the changing shape of neural representations as new skills become familiar.
P. Ellis & H. Zhang
Beyond finding relevant documents: using connections across the literature to surface questions that have yet to be asked.
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