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论文提出 LadderEdit，通过将每个 LoRA 编辑适配器压缩为低秩草图，并在草图不满足重写、泛化和局部性要求时沿阶梯提升秩，实现 5.2 倍的存储节省，并在 50,000 次连续编辑上保持有效。
This paper introduces Memory of Memory (MoM), a framework for LLM agent memory that commits current values on arrival while retaining displaced values as provenance, improving accuracy and reducing stale answers.
This paper introduces HoReN, a parameter-preserving model editing method that uses normalized Hopfield retrieval to handle large-scale sequential updates to large language models. It addresses issues of knowledge accumulation and routing challenges, demonstrating stable performance on 50K sequential edits where prior methods degrade.