MARL: Runtime Middleware That Reduces LLM Hallucination Without Fine-Tuning

MARL, or Model-Agnostic Runtime Middleware for LLMs, is a system designed to reduce hallucinations in language models by implementing a multi-stage self-verification pipeline during runtime without altering the model weights. It can be integrated with any OpenAI API-compatible language model by changing just one line of code, maintaining model-agnostic functionality and allowing for seamless transitions between different models. MARL's architecture involves decomposing a language model call into distinct specialist roles that include hypothesis generation, deep reasoning, auditing, adversarial cross-validation, and synthesis of final responses. This structure addresses the metacognitive gap in AI, where models recognize potential errors but cannot rectify them, thus enhancing error recovery and reasoning accuracy. Unlike traditional approaches requiring fine-tuning or external knowledge, MARL restructures the reasoning process, offering a cost-effective and immediate solution to improve AI performance in high-difficulty tasks. The system is part of a broader initiative linked to FINAL Bench, a benchmark for measuring AI metacognition, and is designed to provide transparency and traceability in AI reasoning processes, offering users insight into why and how decisions are made and errors are corrected.