Tag
This paper presents a neuro-symbolic verification architecture for LLM outputs in high-stakes domains, combining formal symbolic methods with neural semantic analysis. Evaluated on a medical device damage assessment system, it achieves over 83% hallucination detection for structured entities and 30% reduction in report creation time.
The author argues that local AI is underestimated due to usability barriers, and introduces their project Euler to make local AI as seamless as cloud AI with privacy and ownership advantages.
Proposes PDRNN, a modular hybrid AI-assisted pedestrian dead reckoning system that combines a recurrent neural network with separate ML models for orientation, velocity, and distance estimation, with optional radio-based stabilization. Experiments on dynamic sports movement data show superior accuracy and precision compared to classic and ML-based methods.
BEHAVE is a hybrid AI framework for real-time modeling of collective human dynamics, as presented in a preprint on arXiv.