Xi Wang, Songlei Jian, Shasha Li, Xiaopeng Li, Bin Ji, Ma Jun, Xiaodong Liu, Jing Wang, Jianfeng Zhang, Jie Yu, Feilong Bao, Wangbaosheng. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Model editing enables targeted updates to the knowledge of large language models (LLMs) with minimal retraining. Among existing approaches, locate-then-edit methods constitute a prominent paradigm: they first identify critical layers, then compute residuals at the final critical layer based on the target edit, and finally apply least-squares-based multi-layer updates via $\textbf{residual distribution}$. While empirically effective, we identify a counterintuitive failure mode: residual distribution, a core mechanism in these methods, introduces weight shift errors that undermine editing precision. Through theoretical and empirical analysis, we show that such errors increase with the distribution distance, batch size, and edit sequence length, ultimately leading to inaccurate or suboptimal edits. To address this, we propose the $\textbf{B}$oundary $\textbf{L}$ayer $\textbf{U}$pdat$\textbf{E (BLUE)}$ strategy to enhance locate-then-edit methods. Sequential batch editing experiments on three LLMs and two datasets demonstrate that BLUE not only delivers an average performance improvement of 35.59\%, significantly advancing the state of the art in model editing, but also enhances the preservation of LLMs' general capabilities. Our code is available at https://github.com/xpq-tech/BLUE.

Mass unevenness is an important property of yarn and is generally described by the spectrogram and variation-length curve. This paper presented a new method to calculate the spectrogram and variancelength curve of yarn based on the use of multiresolution analysis. The experimental result indicated that the proposed method is not only with higher precision, but also with higher speed than method of Fourier transform and mathematical statistics.

The eigen-problem of max-algebraic matrix in the sense of the completely max-algebra is analyzed in detail in this paper. It is proved that \lambda = \varpi(\gamma) must be the eigenvalue of the matrix A in the sense of the completely max-algebra if real number \varpi(\gamma) is the average weight of the critical circuit ? for some strongly connected subgraph G(A\gamma) in the graph G(A).