Show HN: LoureiroGate – Differentiable Physics Guardrails for PyTorch (github.com)

LoureiroGate, a new PyTorch module, has been introduced as a differentiable safety layer designed to address critical issues in mission-sensitive AI applications. By integrating physics-informed guardrails, LoureiroGate helps prevent AI hallucinations that can lead to dangerous outcomes, such as inappropriate dosage prescriptions in healthcare or unsafe operational commands in robotics. This solution enhances neural networks, which typically operate as statistical interpolators without regard for physical laws, by enforcing constraints during both training and deployment. The core feature of LoureiroGate is its differentiable architecture that enables models to learn safety constraints rather than merely clipping outputs. It incorporates various constraints, such as ValueRangeLimit and RateOfChangeLimit, ensuring that any violation affects the entire batch, thus maintaining robust safety measures. Future updates aim to enhance this functionality further with stateful tracking for real-time inference. By providing a straightforward implementation approach, LoureiroGate represents a significant advancement in making AI systems more reliable and safer across critical sectors.