ROSE：面向多模态大模型终身知识编辑的风险感知正交子空间框架
ROSE: Risk-Aware Orthogonal Subspace Navigation for Lifelong Knowledge Editing in Multimodal Large Language Models
给多模态模型打知识补丁总把旧能力改坏？这篇 ROSE 用正交子空间加风险掩码，连续 1000 次编辑都不崩，代码也放出来了。
论文提出 ROSE 框架，针对多模态大模型终身知识编辑中的局部性侵蚀和灾难性遗忘问题。其核心做法是将参数更新限制在已编辑子空间的正交补空间，并用动态计算的风险掩码保护对模型稳定性关键的事实参数子空间。推理阶段采用原型锚定语义门控和扰动驱动子空间融合的多粒度知识整合策略。在两个多模态基准上，ROSE 在五项关键指标上与 SOTA 方法持平，且在 1,000 次顺序编辑后仍保持稳定性。代码已在 GitHub 开源。
ROSE: Risk-Aware Orthogonal Subspace Navigation for Lifelong Knowledge Editing in Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) require continuous factual updates to stay current, yet lifelong knowledge editing remains a daunting challenge. The primary obstacle is the severe Locality Erosion and Catastrophic Forgetting, where sequential edits inevitably interfere with pre-trained capabilities and prior updates. In this paper, we propose \textbf{ROSE}, a \textbf{R}isk-aware \textbf{O}rthogonal \textbf{S}ubspace navigation framework for lifelong knowledge \textbf{E}diting. ROSE addresses forgetting by aiming to restrict parameter updates to the orthogonal complement of previously edited subspaces. Crucially, it introduces a risk-guided mechanism that utilizes a dynamically computed risk mask to safeguard specific factual parameter subspaces essential for model stability. During inference, ROSE employs a multi-granularity knowledge integration strategy, featuring prototype-anchored semantic gating and perturbation-driven subspace fusion to precisely activate edited knowledge for relevant queries while limiting interference on unrelated inputs. Comprehensive experiments on two prominent multimodal benchmarks show that ROSE is competitive with state-of-the-art approaches across five key metrics, while maintaining outstanding stability over $1,000$ sequential updates. Our code is available at https://github.com/sunlitsong/ROSE.
