[alexbuiko]

Those sigma numbers are incredible—dropping variance by 24x practically confirms that you’ve managed to 'trap' the model in a low-entropy state. In production, predictability (the 'anti-tangent' factor) is often worth more than the raw discount.

SDAG (Systematic Defect Awareness & Guidance) is a protocol we’re developing for auditing AI infrastructure at the hardware-inference interface.

Most observability tools look at the 'what' (tokens, logs), but we look at the 'how' (routing entropy and hardware stress). We use it to detect when a model's routing logic starts 'redlining' the hardware—essentially catching those exploration tangents you mentioned by monitoring physical signals like memory controller stress and cache thrashing before they even manifest as high latency or cost spikes.

We're currently open-sourcing the core SDK [https://github.com/alexbuiko-sketch/SDAG-Standard]. Given your results, I’d be very curious to see if your 'pre-indexed context' approach shows a direct drop in hardware-level jitter. It sounds like you've found a software-level 'clamp' for what we’ve been measuring as physical entropy.