Large language models (LLMs) are impressive, but they sometimes get things wrong or cling to outdated information. Retraining these massive models is incredibly expensive, so researchers have been exploring clever ways to "edit" them—tweaking their internal knowledge without starting from scratch. However, there's a catch: making too many edits can actually make the model worse at other tasks, a problem known as the "general abilities degradation." Imagine teaching a dog a new trick, but it suddenly forgets how to sit or fetch! This research paper dives into why this happens, pointing to a mathematical concept called the "condition number" of a matrix. Think of it like a measure of how sensitive the model's knowledge connections are to change. As edits pile up, this number grows, making the model more fragile and prone to forgetting. To solve this, the researchers introduce PRUNE (Perturbation Restraint on Upper bouNd for Editing). PRUNE acts like a stabilizer, carefully controlling the edits to minimize disruption to the model's existing knowledge. The results are promising: PRUNE allows LLMs to learn new things while retaining their overall smarts, opening doors for more adaptable and continuously learning AI.