Dimitri Kachler

Language models struggle with lifecycle reasoning in complex systems, often faltering as interaction complexity increases, but additional inference effort can unlock significant performance gains.

Reasoning Core achieves unprecedented performance in completion-supervised reasoning tasks, outpacing existing procedural datasets and revealing critical design insights for effective training.

Influcoder achieves rapid and compact data attribution for LLMs, transforming how we identify the influence of training samples on model behavior.