Michael Hahn

It is shown that data-agnostic methods, such as parameter averaging and dynamic selection, often fail to combine knowledge from logically disjoint fine-tuning datasets, and that LoRA reuse relies more on shallow pattern matching than on logical integration of existing knowledge.

LLM-based peer review systems can be made significantly more robust against adversarial manipulation via a co-evolutionary GAN approach that anticipates novel attacks.

Chain-of-Thought reasoning in Transformers hits a surprising expressivity ceiling when generalizing to longer sequences, unless you let your vocabulary grow with the problem size and use "signpost" tokens.