Post-training for large language models (LLMs) is constrained by the high cost of acquiring new knowledge or correcting errors and by the unintended side effects that frequently arise from retraining. To address these issues, we introduce REPAIR (Robust Editing via Progressive Adaptive Intervention and Reintegration), a lifelong editing framework designed to support precise and low-cost model updates while preserving non-target knowledge. REPAIR mitigates the instability and conflicts of large-scale sequential edits through a closed-loop feedback mechanism coupled with dynamic memory management. Furthermore, by incorporating frequent knowledge fusion and enforcing strong locality guards, REPAIR effectively addresses the shortcomings of traditional distribution-agnostic approaches that often overlook unintended ripple effects. Our experiments demonstrate that REPAIR boosts editing accuracy by 10%-30% across multiple model families and significantly reduces knowledge forgetting. This work introduces a robust framework for developing reliable, scalable, and continually evolving LLMs.

翻译：大型语言模型（LLM）的后训练常受限于获取新知识或修正错误的高昂成本，以及重训练频繁引发的非预期副作用。为解决这些问题，我们提出了REPAIR（通过渐进式自适应干预与再整合实现稳健编辑），这是一种终身编辑框架，旨在支持精确且低成本的模型更新，同时保护非目标知识。REPAIR通过闭环反馈机制结合动态内存管理，缓解了大规模顺序编辑的不稳定性与冲突。此外，通过融入频繁的知识融合并实施强局部性防护，REPAIR有效解决了传统分布无关方法常忽视非预期连锁效应的缺陷。实验表明，REPAIR在多个模型家族中将编辑准确率提升了10%-30%，并显著降低了知识遗忘。本研究为开发可靠、可扩展且持续演进的大型语言模型提供了一个稳健的框架。