Interpretable Reasoning in Large Language Models: A Neurosymbolic Approach

Large Language Models (LLMs) have demonstrated remarkable proficiency across a spectrum of reasoning tasks, yet their underlying decision-making processes remain largely opaque, hindering their deployment in high-stakes domains where trust and verifiability are paramount. While post-hoc interpretability methods offer surface-level explanations, they often fail to faithfully capture the causal mechanisms driving model outputs. This paper investigates a neurosyntolic approach to rendering LLM reasoning intrinsically more interpretable. We systematically compare two architectural paradigms: (i) an integrative framework, wherein symbolic reasoning constraints are embedded directly within the neural network's weights and attention mechanisms, and (ii) a hybrid framework, which couples a pre-trained LLM as a natural language parser and semantic encoder with an external, differentiable symbolic solver that executes explicit, rule-based inference chains. Through a series of controlled experiments on deductive and arithmetic reasoning benchmarks, we evaluate each paradigm along three dimensions: reasoning accuracy, step-wise interpretability, and generalizability to out-of-distribution inputs. Our findings indicate that while the integrative approach offers marginal efficiency gains, the hybrid framework yields substantially superior interpretability by exposing a verifiable, human-readable chain of symbolic operations that is disentangled from the neural network's parametric knowledge. Furthermore, we demonstrate that this hybrid architecture preserves the compositional flexibility of the underlying LLM while enabling error localization and targeted correction. We conclude by proposing a set of design principles for building next-generation LLMs that are not merely high-performing, but also intrinsically interpretable through the structured synergy of neural perception and symbolic reasoning.