While these systems show 100% reliability in Isaac Lab simulations, real-world deployment on H100 hardware reveals what I call "Stochastic Logic Drift."
The core issue is that in standard Euclidean-based vector search and reasoning manifolds, floating-point non-determinism and thermal noise accumulate.
After approximately 4 hours of continuous high-load inference on H100 PCIe, the decision manifold loses its deterministic lock. I’ve observed bit-level similarity (LCP) decay significantly as hardware entropy gains ground over model weights.
I’ve published a full forensic audit—including a SHA-256 hash-chain of over 28,000 queries—that demonstrates this “Logic Collapse” on baseline systems versus a p-adic invariant approach that maintains a 100% bit-perfect lock even under peak thermal stress (72°C).
The raw telemetry, H100 hardware receipts, and the CUDA kernel logic I used to anchor the substrate are available here: https://gist.github.com/StanByriukov02/3686a8cd3da70effa5d848deb46753e7
If we continue to ignore "Inference Liability" at the hardware level, we are building autonomous systems on a foundation of sand. Simulation parity is a myth if the substrate itself isn't deterministic.
I’m interested to hear if others have seen similar jitter in long-context reasoning runs on sm_90/sm_100, or if the industry is simply accepting this drift as "expected noise."