  Chain-of-Thought (CoT) prompting has emerged as a pivotal technique for
augmenting the inferential capabilities of language models during reasoning
tasks. Despite its advancements, CoT often grapples with challenges in
validating reasoning validity and ensuring informativeness. Addressing these
limitations, this paper introduces the Logic Agent (LA), an agent-based
framework aimed at enhancing the validity of reasoning processes in Large
Language Models (LLMs) through strategic logic rule invocation. Unlike
conventional approaches, LA transforms LLMs into logic agents that dynamically
apply propositional logic rules, initiating the reasoning process by converting
natural language inputs into structured logic forms. The logic agent leverages
a comprehensive set of predefined functions to systematically navigate the
reasoning process. This methodology not only promotes the structured and
coherent generation of reasoning constructs but also significantly improves
their interpretability and logical coherence. Through extensive
experimentation, we demonstrate LA's capacity to scale effectively across
various model sizes, markedly improving the precision of complex reasoning
across diverse tasks.
