Show HN: A deterministic physics kernel for Industrial AI (0 violations vs. 59) (github.com)

A new repository, AXIOM RE, has been introduced, featuring a deterministic physics kernel designed specifically for ensuring the safety of AI applications in critical industrial sectors, such as water, energy, and mining. Unlike traditional AI approaches that rely on probabilistic models, which pose unacceptable risks in zero-tolerance environments, AXIOM decouples control logic from physical laws, implementing a "Constitutional Layer" to guarantee that AI actions adhere strictly to established physical invariants. This architecture functions through three interconnected layers: an external agent that proposes actions, a constitutional bridge that verifies these actions, and a sealed Physics Kernel that processes and returns raw physics data. Significantly, AXIOM has demonstrated its efficacy by achieving zero safety violations in its controlled experiments, starkly contrasting with standard reinforcement learning (RL) methods that recorded 59 violations under similar conditions. The system not only strengthens compliance with safety standards but also accelerates convergence times through guided action validation instead of trial and error. This represents a critical advancement in the quest for reliable and safe AI implementations across industries where even minor errors can lead to catastrophic outcomes.