Synergizing Symbolic Logic and Reinforcement Learning for Provably Correct Reasoning in Large Language Model Decision Pipelines
DOI:
https://doi.org/10.66280/cis.v1i1.199Abstract
The rapid integration of Large Language Models (LLMs) into critical decision-making infrastructures has highlighted a fundamental tension between the probabilistic fluidity of neural architectures and the categorical precision required for high-stakes governance. While transformer-based models excel at pattern recognition and linguistic synthesis, they remain prone to stochastic hallucinations and logical inconsistencies that undermine their utility in regulated environments. This research paper explores a hybrid architectural paradigm that synergizes symbolic logic with reinforcement learning to establish provably correct reasoning pathways within LLM decision pipelines. By embedding formal axiomatic constraints into the reward structures of reinforcement learning frameworks, the proposed system ensures that the generative output of the model adheres to predefined logical boundaries without sacrificing the creative flexibility of the underlying neural network. The study provides a comprehensive system-level analysis of this neuro-symbolic synthesis, focusing on the structural trade-offs between computational efficiency and formal rigor. We examine the deployment of these systems in socio-technical infrastructures such as autonomous legal adjudication, precision biosecurity auditing, and financial risk management. Furthermore, the paper addresses the policy implications of shifting from black-box probabilistic models to auditable, logic-constrained systems. Our findings suggest that this synergy not only enhances the robustness and reliability of automated reasoning but also provides a scalable framework for ensuring fairness and accountability in large-scale artificial intelligence deployments. The discussion concludes with a forward-looking perspective on the sustainability of hybrid architectures in an increasingly complex global information ecosystem.
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