Internal Stabilization Framework for Reducing Hallucination in Large Language Models

This paper introduces a revised framework for suppressing hallucination in large language models (LLMs) by treating hallucination not merely as an output error but as a manifestation of internal representational instability. Conventional mitigation approaches target external signals—retrieval augmentation, instruction tuning, or post-hoc verification—while overlooking the deeper architectural causes that give rise to semantic drift. Our framework reconceptualizes hallucination as a structural inconsistency within the model’s internal generative dynamics and proposes a stability-oriented approach grounded in representational coherence. The paper formalizes cross-layer semantic divergence as the root factor of hallucination and develops an internal stabilization method that actively constrains drift as tokens are generated. This approach aims not only to suppress hallucination but also to preserve creativity, long-context reasoning, and domain adaptability. Preliminary evaluations across open-domain reasoning tasks demonstrate promising performance, indicating that internal stabilization significantly enhances reliability without compromising generative flexibility. This work provides a foundational step toward building LLMs that are not only powerful but also trustworthy, transparent, and structurally resilient.