arXiv:2607. 03148v1 Announce Type: cross Abstract: Activation functions are considered an essential primitive for neural nonlinearity, i.
By Muhammad Sabih, Frank Hannig, J\"urgen Teich
The paper introduces GAPS, a dimension‑level gating approach for activation steering in language models. GAPS uses two training‑free gates—a static separability gate based on AUROC and a dynamic posterior gate based on a Gaussian model—to selectively apply steering vectors only to neurons that carry reliable concept information or are currently mis‑activated. Experiments on Gemma‑3 and Qwen‑3 show that GAPS improves or matches the performance of token‑level methods, notably reducing Gemma‑3’s toxicity rate from 6.52% to 0.48% under a fixed capability budget.
By Moghis Fereidouni, Muhammad Umair Haider, Hassan Sajjad, A. B. Siddique
arXiv:2603. 06861v2 Announce Type: replace Abstract: Activation functions are fundamental to deep neural networks, governing gradient flow, optimization stability, and representational capacity.
By Mingi Kang, Zai Yang, Jeova Farias Sales Rocha Neto
arXiv:2606.28444v2 Announce Type: replace-cross
Abstract: Classical universal approximation theorems (UAT) establish the expressive power of sigmoidal multilayer perceptrons, but they do not specify...
By Yi-Shan Chu
arXiv:2606. 20292v1 Announce Type: new Abstract: The use of neural networks (NNs) is rapidly increasing, including in safety- and security-critical domains.
By Philipp Kern, L\'aszl\'o Antal, Erika \'Abr\'aham, Carsten Sinz
arXiv:2602.17493v2 Announce Type: replace-cross
Abstract: We develop a method for training neural networks on Boolean data in which the values at all nodes are strictly $\pm 1$, and the resulting mod...
By Veit Elser, Manish Krishan Lal