arXiv:2607. 01799v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose internal activations of neural networks into sparse linear combinations of learned features by fitting an overcomplete dictionary $\mathbf{W}\in\mathbb{R}^{m\times n}$ with $m<n$, and inferring a sparse code $\mathbf{x}\in\mathbb{R}^n$ from $\mathbf{h}\approx\mathbf{W}\mathbf{x}$.
By Rodrigo Mendoza-Smith
arXiv:2609.07762v1 Announce Type: cross
Abstract: Recurrent detectors such as bidirectional long short-term memory (Bi-LSTM) networks are low-complexity alternatives to the optimal Bahl-Cocke-Jelinek...
By Nurettin Safak, Osman Tokluoglu, Enver Cavus
arXiv:2606. 02628v1 Announce Type: new Abstract: We investigate whether open-source LLMs encode a linearly separable truthfulness signal in their hidden states, and at which network depth this signal is strongest.
By Aizierjiang Aiersilan
arXiv:2607. 16761v1 Announce Type: cross Abstract: Dropout and Random Gradient Masking (RaM) are two training techniques used to improve performance in deep learning.
By Javier Maass, L\'ena\"ic Chizat
arXiv:2608. 06177v1 Announce Type: new Abstract: Binary neural networks are very attractive for constrained deployment, enabling small footprint and low-power inference.
By Quentin Luquet de Saint-Germain, Massil Ait Abdeslam, Jean Pierre David
The Recurrent Looped Transformer (RLT) divides its layers into a parallel causal encoder and a recurrent decoder, allowing the computation path to grow with sequence length while keeping a fixed per-token cost. Experiments on six algorithmic tasks show that RLT outperforms a standard eight‑layer Transformer, achieving perfect generalization on parity up to 256 bits and high accuracy on swap‑based permutation tracking and modular arithmetic beyond training lengths. Ablation studies confirm that the recurrent feedback is essential for these gains, with chunked updates preserving performance on parity but reducing accuracy on permutation tracking.
By Yifan Zhang, Jichen Feng, Shihan Qin