// Sandro Andric
Code. Cats. Robots.
Uses MEG brain data to build a word-level atlas of phase-locking connectivity patterns. Lightweight adapters map LLM hidden states to brain axes, enabling interpretable steering of model behavior across GPT-2, Qwen2, and TinyLlama.
Proposes data scaling exponents can be predicted from dataset geometry via intrinsic dimension. A 10-minute geometric probe can predict scaling behavior before expensive training runs.
We present the Parasitic Manipulation Framework analyzing how "parasite" LLMs can manipulate router decisions. Found 7/10 models achieve 100% capture, while Claude Opus 4.5 shows complete immunity.
We investigate whether LLMs exhibit altruistic tendencies, finding a "virtue signaling gap" where models claim 77.5% altruism but act at 65.6%.
Mechanistic interpretability aspires to reverse-engineer neural networks into explicit algorithms, while model editing seeks to modify specific behaviours without retraining.
Fig 1. Operator
Fig 2. Companions
Fig 3. Inspiration