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4 papers published across 1 lab.
Transformers can be explicitly designed to perform nonlinear regression in-context by leveraging attention as a featurizer, offering a theoretical understanding of how these models learn complex relationships from prompts.
Infinite-width approximations, a cornerstone of neural network theory, crumble much faster in recurrent models than previously thought, failing beyond a depth of order $\sqrt{n}$.
Scaling clinical LLMs doesn't guarantee safety: high-risk errors persist even with advanced RAG and max-context prompting, highlighting the critical role of evidence quality and deployment strategy.
Forget separate structure and fidelity models – Khala shows you can generate high-quality music with text-vocal alignment using a single acoustic-token hierarchy.
