Multi-round batch knowledge editing often suffers from performance degradation as edits accumulate. Focusing on the locate-then-edit paradigm, we analyze this phenomenon from a spectral perspective and identify a previously overlooked structural factor: the intrinsic knowledge of the model and historical edit memories exhibit markedly different spectral characteristics and information distributions, yet are naively coupled and jointly inverted during editing. Based on this insight, we propose SpecEdit to improve the model editing from a spectral perspective. SpecEdit performs spectral decoupling to isolate editing-critical directions and reduce destructive coupling, followed by spectral-structure-aware information compensation and spectral fusion to construct a refined closed-form solution. The module integrates seamlessly into existing editing methods without altering their original optimization procedures. Experiments on multiple LLMs and editing methods show that SpecEdit consistently improves performance, demonstrating that modeling spectral structure is an effective, interpretable approach and a promising direction for future research.

Knowledge editing emerges as a promising approach for updating target knowledge in Large Language Models (LLMs) in a timely manner, thereby preventing undesirable behaviors stemming from outdated, inaccurate, or incomplete knowledge. However, existing methods mainly focus on instance-level editing, which is prone to over-editing risk featuring knowledge degradation and general ability deterioration, due to redundant instance-specific modifications for knowledge. To mitigate the over-editing risk, we explore the rule-level editing problem that avoids case-by-case modification by generalizing rule-level knowledge to update rule-derived instances. We further construct a benchmark called RuleEdit for systematic evaluation on rule-level editing. Moreover, we propose a Rule-Transfer Editing (RTE) method to facilitate effective updates and generalizations of rule-level knowledge in LLMs. Experimental results highlight our significant improvements, with the enhancements of 28.1% in portability and 8.1% in average performance over the best-performing baselines for LLaMA-2-7B on RULE_mix.

Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions (i.e., CAD programs). In practical design workflows, designers often engage in time-consuming reviews and refinements of these prototypes by comparing them with reference images. To bridge this gap, we introduce the CAD review task to automatically detect and correct potential errors, ensuring consistency between the constructed 3D objects and reference images. However, recent advanced multimodal large language models (MLLMs) struggle to recognize multiple geometric components and perform spatial geometric operations within the CAD program, leading to inaccurate reviews. In this paper, we propose the CAD program repairer (ReCAD) framework to effectively detect program errors and provide helpful feedback on error correction. Additionally, we create a dataset, CADReview, consisting of over 20K program-image pairs, with diverse errors for the CAD review task. Extensive experiments demonstrate that our ReCAD significantly outperforms existing MLLMs, which shows great potential in design applications.