Our latest paper "Towards principled knowledge editing methods for large language model reasoning" is now published in Nature Machine Intelligence. 🧠 @NatMachIntell 📄 nature.com/articles/s42256-0… As AI moves toward reasoning, agents, parameter memory, and recursive self-improvement (RSI), a fundamental question is becoming increasingly important: If an AI can improve itself, how should it revise what it already “knows”? Our Perspective argues that knowledge in modern reasoning models is not a collection of isolated facts stored in independent parameter slots. It is an entangled, belief-dependent, and context-sensitive system. This calls for a shift: From isolated facts → entangled knowledge From updating knowledge → revising beliefs From parameter updates → evolving parameter memory We highlight three directions for next-generation knowledge editing: → Knowledge entanglement: update interconnected knowledge circuits (structures) rather than isolated facts. → Belief-aware editing: understand model confidence and intervene differently depending on what the model already believes. → Reasoning-augmented revision: use reasoning, evidence, and contextual scaffolding to make knowledge updates coherent. The deeper question is not simply how to give AI more memory. It is: Can AI understand what it believes, why it believes it, and how to revise those beliefs coherently? This may become a critical foundation for continual learning, controllable AI, parameter memory, and recursive self-improvement. Would love to hear thoughts from the communities working on LLM reasoning, memory, model editing, continual learning, mechanistic interpretability, agents, and RSI. #NatureMachineIntelligence #NatureMI #NLP #LLM #KnowledgeEditing #MechanisticInterpretability #KnowledgeCircuits #Circuits