Researchers have developed a new framework called Defactualize-Steer-Rehydrate (DSR) to improve the performance of large language models in fact-sensitive applications. DSR integrates a knowledge graph with activation steering to control style and preserve factual correctness. The framework extracts salient entities, replaces them with placeholders, and then restores verified values after generation. In experiments, DSR significantly improved the recovery rate of verified entities compared to a baseline method, while maintaining effective style control across different model families. --- Why it matters: This matters because it addresses a long-standing challenge in developing trustworthy conversational AI systems that can generate responses both factually correct and stylistically controlled. Engineers working on such systems will be interested in the framework's ability to systematically enhance reproducible generative AI without requiring model fine-tuning. Source: https://arxiv.org/abs/2608.20393