Symbolic Solver

Symbolic solvers are computational tools that leverage formal logic and mathematical rules to solve complex problems, often by combining with large language models (LLMs). Current research focuses on hybrid architectures that integrate LLMs for natural language processing and problem representation with symbolic engines for precise, verifiable reasoning, employing techniques like backward chaining and transformer-based action search. This synergistic approach aims to improve the accuracy, explainability, and efficiency of problem-solving in domains such as mathematical reasoning, scheduling, and logical deduction, ultimately advancing both AI capabilities and human-computer interaction.