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University of Cambridge
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Slow features in Persistent SAEs can maintain detection signals over long contexts, revolutionizing how we interpret and monitor language models.
Injecting domain-specific knowledge into small tabular models can lead to substantial performance gains in niche applications, highlighting the importance of tailored fine-tuning strategies.
A unified theory of interpretability that leverages Lagrangian mechanics to transform opaque models into interpretable ones, revealing new research avenues and design principles.
Injecting spatial transcriptomics data into existing pathology foundation models unlocks significant performance gains across a range of downstream tasks, including molecular status prediction and gene-to-image retrieval.
