"Internal Stabilization Framework for Reducing Hallucination in Large Language Models"

AbstractHallucination remains one of the most fundamental limitations of modern Large Language Models (LLMs), yet most mitigation strategies focus only on external signals such as retrieval or post-hoc verification. This paper introduces a breakthrough internal stabilization framework that directly targets representational drift — the hidden-state instability that causes hallucination inside the model’s own generative process. We formalize drift as a measurable cross-layer divergence and demonstrate how internal semantic trajectories can be dynamically stabilized without reducing creativity or impairing reasoning depth. Our approach constructs a multi-stage semantic checkpointing mechanism and a consistency-preserving feedback loop that actively steers the model’s internal geometry toward stable, truth-aligned states. Experiments across legal, medical, financial, and open-domain benchmarks show up to 50% hallucination reduction while maintaining generative fluency and high-resolution reasoning. By shifting the discussion from external patching to internal cognitive stabilization, this work opens a new research direction for building trustworthy, self-consistent, and cognitively grounded AI systems.