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We study the numerical approximation of scalar conservation laws by computational optimization of the numerical flux function in a first-order finite volume method.

We investigate how Large Language Models internally represent entities by introducing entity mention reconstruction as a novel framework for studying how LLMs encode and manipulate entities.

We introduce ToMMeR, a lightweight model probing mention detection capabilities from early LLM layers, achieving high zero-shot recall across diverse benchmarks.

We propose MICE (Minimal Interaction Cross-Encoders), a new architecture that achieves a 4x decrease in inference latency while maintaining high ranking effectiveness.

We reproduce and compare different distillation strategies for cross-encoders across a wide range of architectures, identifying robust design choices for effective re-ranking.

This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!

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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.

This is a description of a teaching experience. You can use markdown like any other post.

This is a description of a teaching experience. You can use markdown like any other post.