arXiv:2607. 23877v1 Announce Type: new Abstract: We present Flash-CNNCap, a CNN-based capacitance extractor that reformulates full-matrix capacitance prediction as image-to-image regression over spatial contribution maps.
By Hector R. Rodriguez, Jiechen Huang, Wenjian Yu
arXiv:2605. 18848v3 Announce Type: replace Abstract: This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by exploiting the exact decomposition property of kernel functions, thereby eliminating approximation error.
By Weinuo Ou
arXiv:2608. 03913v1 Announce Type: new Abstract: Dense pretrained transformers do not naturally expose interpretable units for circuit extraction.
By Chuanhao Yan, Xuhan Huang, Yawen Duan, Zhenfei Yin, Hang Zhao, Bryan Dai, Jie Fu
arXiv:2603. 07523v3 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales.
By Jianlu Shen, Fu Feng, Yucheng Xie, Jiaqi Lv, Xin Geng
arXiv:2606. 19538v1 Announce Type: new Abstract: Convolutional networks, recurrent networks, and transformers each encode different inductive biases -- locality, sequential memory, and content-dependent pairwise interaction -- and have remained mathematically distinct since their inception.
By Ashim Dhor, Rasel Mondal, Pin Yu Chen
arXiv:2510. 25013v2 Announce Type: replace-cross Abstract: Mechanistic interpretability aims to reverse-engineer large language models (LLMs) into human-understandable computational circuits.
By Rabin Adhikari