KM news
On September 4, 2026, researchers at Stanford University published a study in Nature Machine Intelligence detailing a new framework called "Recursive Knowledge Distillation" (RKD). This method addresses the "model collapse" observed in enterprise AI systems that rely heavily on synthetic data. RKD implements a "human--in--the--loop" verification layer that identifies and preserves "tacit organizational knowledge"—the unwritten expertise and context that traditional Large Language Models (LLMs) often lose during iterative fine--tuning. The research demonstrates a 40% improvement in the accuracy of AI--generated technical documentation by prioritizing high--fidelity human inputs over high--volume synthetic outputs, ensuring that organizational memory remains robust as AI agents take over more administrative tasks.