Abstract: Incorporating prior knowledge and domain-specific input-output requirements, such as safety or stability, as hard constraints into neural networks is a key enabler for their deployment in high-stakes applications. However, existing methods often rely on soft penalties, which are insufficient, especially on out-of-distribution samples. In this talk, I will introduce hard-constrained neural networks (HardNet), a general framework for enforcing hard, input-dependent constraints by appending a differentiable enforcement layer to any neural network. This approach enables end-to-end training and, crucially, is…

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Abstract: Large language models (LLMs) often concentrate their attention on a small set of tokens—referred to as attention sinks. Common examples include the first token, a prompt-independent sink, and punctuation tokens, which are prompt-dependent. Although these tokens often lack inherent semantic meaning, their presence is critical for model performance, particularly under model compression and KV-caching. Yet, the function, semantic role, and origin of attention sinks—especially those beyond the first token—remain poorly understood. In this talk, I’ll present a comprehensive investigation…

Abstract: Compute scaling has dominated the conversation with modern language models, leading to an impressive array of algorithms that optimize performance for a given training (and sometimes inference) compute budget. But as compute has grown cheaper and more abundant, data is starting to become a bottleneck, and our ability to exchange computing for data efficiency may be crucial to future model scaling. In this talk, I will discuss some of our recent work on synthetic data and algorithmic approaches to…

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