01
The Geometry of In-Context Learning
A geometric account of how transformers construct and update implicit task representations from demonstrations.
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148,291 papers across artificial intelligence, mathematics, and the ideas between them.
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02
Scaling Laws for Neural Operators
Empirical and theoretical scaling laws for learning maps between infinite-dimensional function spaces.
03
Proof Automation with Language Models
A search-and-verification framework that turns informal mathematical intuition into machine-checkable proofs.
04
Spectral Bias Beyond Euclidean Domains
Characterizing the frequency preferences of graph and manifold neural networks across non-Euclidean domains.
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