Trustworthy Reasoning and Hallucination Mitigation in Large Language Models

Abstract

Large language models are increasingly embedded in decision support systems across medicine, law, science, and public administration. Their capacity to produce fluent and seemingly well-reasoned text has made them attractive components of socio-technical infrastructures. At the same time, hallucination remains a persistent source of risk, and trustworthy reasoning cannot be achieved through a single architectural or algorithmic intervention. This paper provides a systems-level analysis of reasoning reliability and hallucination mitigation in large language models. It examines why hallucination arises from the interaction between generative pretraining, probabilistic decoding, evaluation constraints, and deployment context. The discussion moves beyond model accuracy to consider the structural trade-offs associated with reasoning depth, inference cost, verification, alignment, runtime guardrails, and institutional oversight. The paper reviews the role of chain-of-thought and search-based reasoning, verifier training, process supervision, human feedback alignment, lightweight trajectory filtering, self-evaluation, and continuous deployment monitoring. It further addresses fairness, robustness, sustainability, and policy implications of building reliable language model systems. The central argument is that hallucination should be treated as an emergent property of a larger pipeline rather than a narrow defect of a single model. Trustworthy reasoning therefore requires layered technical safeguards, transparent documentation, and governance mechanisms that enable failures to be detected, contained, and corrected before they produce institutional harm. Future directions include adaptive risk-based verification, stronger separation between linguistic generation and logical inference, cross-domain evaluation, and policy frameworks that focus on systemic accountability rather than isolated benchmark performance.