Knowledge editing updates facts in large language models while preserving unrelated knowledge. Most prior work studies object editing, which modifies the object under a fixed subject–relation pair. We instead study relation replacement editing, where an obsolete relation must be suppressed as a new one is introduced. We systematically study this setting with a dedicated benchmark and method. We introduce ReEditBench to evaluate replacement rather than association addition. Existing editors exhibit two major failures: obsolete-relation retention remains as high as 98.20% after successful edits, and difficult samples often destabilize editing. To address this, we propose R²Edit, a two-stage framework for relation replacement. Its target-smoothed Forget stage reduces obsolete-relation activation without fixed/random pseudo-target bias, while its curriculum-guided Edit stage progressively injects new relations from easy to hard samples. Across multiple LLMs, R²Edit improves replacement success, reduces retention, and preserves generalization. Stress tests on difficult subsets further demonstrate the robustness of the Edit stage. Code is available at: https://github.com/caoxu0109/R2Edit.

Fine-tuning safety aligned large language models (LLMs) on downstream data improves adaptation but may erode learned safety behavior. Existing methods use fixed safety examples, global constraints, or one-sided task filtering. Our diagnostics show task updates expose different safety constraints, motivating joint selection of relevant references and compatible task samples. We propose DualSelect, a coupled framework for task and reference selection that refreshes task conditioned safety references before filtering whole task samples compatible with the induced reference direction. Under a minimax view, DualSelect selects safety references with high preservation loss and task conflict, together with compatible task samples, through entropy-regularized scoring surrogates, lazy reference refresh, and gradient correction. Across the evaluated English instruction-following and mathematical-reasoning settings on 1B–8B LLMs, DualSelect better preserves safety while maintaining task utility; using the REDORCA judge, it improves Safety Avg. over the strongest baseline by at least 5.10 points and remains highest in Safety Avg. across judges with moderate overhead. This view extends to retention focused continual learning. Code at: https://github.com/ChnIRuI/DualSelect.

State-of-the-art Large Multi-Modal Models (LMMs) have demonstrated exceptional capabilities in vision-language tasks. Despite their advanced functionalities, the performances of LMMs are still limited in challenging scenarios that require complex reasoning with multiple levels of visual information. Existing prompting techniques for LMMs focus on either improving textual reasoning or leveraging tools for image preprocessing, lacking a simple and general visual prompting scheme to promote vision-language coordination in LMMs. In this work, we propose SCAFFOLD prompting that scaffolds coordinates to promote vision-language coordination. Specifically, SCAFFOLD overlays a dot matrix within the image as visual information anchors and leverages multi-dimensional coordinates as textual positional references. Extensive experiments on a wide range of challenging vision-language tasks demonstrate the superiority of SCAFFOLD over the textual Chain-of-Thought prompting.