Modulo Theory

Modulo theory research extends logical reasoning by incorporating rich mathematical constraints, enabling more expressive and powerful formal verification and reasoning systems. Current efforts focus on developing efficient decision procedures and knowledge compilation techniques for various logics, such as Linear Temporal Logic (LTL) modulo theories, often employing tableau-based methods and integrating Satisfiability Modulo Theories (SMT) solvers with machine learning models. This work is significant for enhancing the safety and reliability of AI systems, improving the efficiency of automated reasoning tasks, and enabling more sophisticated analysis of complex systems with data-dependent behavior.