Toward Zero Hallucination: PRISM Gate for Permissioned and Trustworthy LLM Reasoning

This work introduces PRISM Gate, a lightweight, model-agnostic filtering layer for reducing hallucinations in large language models (LLMs). Rooted in the dynamic geometry of reasoning, PRISM Gate evaluates candidate outputs through multidimensional consistency checks that jointly enforce structural, factual, and semantic integrity. Unlike retraining- or retrieval-based methods, PRISM Gate operates purely at the output level and abstains when admissibility is low, rather than returning a potentially incorrect answer. Across experiments in arithmetic reasoning, historical question answering, and abstractive summarization, PRISM Gate reduces hallucination rates by up to 78% while maintaining high precision on accepted responses. Most notably, on three logical reasoning benchmarks (LogiQA, ReClor, and StrategyQA), PRISM Gate achieves 0% hallucination at full coverage, showing that dynamically regulated admissibility surfaces can completely eliminate inconsistency in multi-step inference. Taken together, these results establish PRISM Gate as an adaptive, black-box framework for safer, self-regulated, and trustworthy language models, one that explicitly maps and constrains the evolving geometry of reasoning itself.