Researchers at the Korea Advanced Institute of Science and Technology have unveiled a novel training framework designed to resolve a critical bottleneck in the development of personalized artificial intelligence. As organizations increasingly fine-tune large language models on proprietary or individual datasets to enhance task-specific performance, the process frequently degrades the model pre-existing safety protocols. A team led by Professor Changick Kim of KAIST School of Electrical Engineering, with first author doctoral student Seokil Ham, has engineered a solution that preserves customized capabilities while actively strengthening safety guardrails. The research will be featured as a Spotlight presentation at the International Conference on Machine Learning in 2026. The core innovation lies in the Buffer-and-Reinforce methodology, which leverages a counterintuitive finding regarding model behavior during adversarial states. Instead of allowing harmful user data to directly overwrite the base model safety alignments, the framework temporarily isolates the model using a specialized module named BufferLoRA. This component operates as a protective buffer during the fine-tuning phase, allowing the system to absorb new, task-specific instructions without inadvertently learning malicious patterns. Once the customization process concludes, BufferLoRA is discarded. Following the buffering stage, the team applies a second component, ReinforceLoRA, to reconstruct and amplify the model safety parameters. This phase utilizes QR decomposition, a mathematical technique that isolates distinct information streams, ensuring that only the newly acquired functional knowledge is retained while safety protocols are deliberately reinforced. The dual-stage approach effectively decouples capability learning from safety degradation. Experimental validation demonstrated the framework robustness under extreme conditions. When fine-tuned exclusively on harmful query-response pairs, the model generation of unsafe content dropped to approximately eight percent, significantly outperforming the baseline fine-tuned model, which produced harmful responses at a rate of roughly eighteen percent. The framework achieved state-of-the-art safety metrics and maintained high customization accuracy without requiring additional curated safety datasets or imposing substantial computational overhead. Professor Kim emphasized that this architecture establishes a foundational technology for scalable, secure AI customization. By eliminating the traditional trade-off between model personalization and operational safety, the Buffer-and-Reinforce framework enables enterprises and individual developers to deploy customized language models with confidence. The technology is positioned to accelerate the adoption of reliable, agent-driven AI services in commercially sensitive environments, marking a decisive step toward trustworthy personalized artificial intelligence.