LLM Neuroanatomy: How I Topped the AI Leaderboard Without Changing a Single Weight

A researcher achieved #1 on the HuggingFace Open LLM Leaderboard by duplicating seven middle layers of a 72-billion parameter AI model without changing any weights or training data. After observing that language models could reason in Base64 encoding and that unconventional layer arrangements still functioned, they hypothesized that transformers have distinct functional regions - early layers for input processing, late layers for output generation, and middle layers for abstract reasoning. Using custom "brain scanning" techniques on consumer GPUs, they discovered that repeating layers 45-52 improved performance across benchmarks, with gains up to 17.72% on some tasks.