A decade since AlphaGo, and we’re still just scratching the surface of model alignment and efficiency. While DeepMind proved that AI can master intuition, we are now looking at the other side: the physical cost of that intuition.
I’m currently preparing a 'match' against KataGo, but not by out-calculating it in the traditional sense. We are using SDAG [https://github.com/alexbuiko-sketch/SDAG-Standard] to monitor routing entropy and hardware stress during high-load inference. The goal is to see if we can identify 'hallucination paths' through hardware-level signals before they manifest in the game logic. It feels like the next frontier of auditing: treating LLMs/RL agents not just as math, but as physical systems with detectable stress patterns.
