Probing whether vision-language models answer from visual evidence, language priors, or spurious visual representations.
Using chess and Chess960, we investigate how strategic concepts appear across model layers and how stronger play can diverge from human conceptual understanding.
Connecting attention sinks to representation compression to explain how information changes across LLM layers.
Studying which layers support mathematical reasoning and how their roles persist after post-training.