hyperbolix is an open‑source library for hyperbolic deep learning in JAX, built on Flax NNX. It provides six manifolds—including Euclidean, Poincaré ball, hyperboloid, κ‑stereographic, mixed‑curvature product, and proper velocity space—through a common interface, and implements a wide range of layer families (linear, convolution, attention, normalization, positional encoding, regression, vector quantization). The library also supplies Riemannian optimizers, wrapped distributions, dimensionality‑reduction techniques, and precision‑tested operations that replace numerically unstable formulas on the hyperboloid, ensuring accurate float32 computations at large distances.
By Timo Klein, Thomas Lang, Yllka Velaj, Sebastian Tschiatschek
The paper investigates minimal‑norm interpolation and λ2‑regularized logistic‑loss minimization for binary classification using univariate two‑layer ReLU networks. It provides exact geometric characterizations of optimal classifiers, showing that unpenalized hidden‑layer biases yield continuous piecewise‑affine functions that tightly follow label switches, while penalized biases produce a unique, sparsest classifier with a single kink per same‑label segment. Adding a free affine skip connection does not change these function‑space solutions but guarantees that every KKT point becomes globally optimal, eliminating suboptimal KKT points that can arise without the skip connection.
By Karolina Drabik, Ben Lewis, Antoni Puch, Etienne Boursier, Piotr Hofman, Matthias Englert, Ranko Lazi\'c
The study investigates whether sensory-aligned receptive fields provide computational benefits beyond mere resource efficiency in recurrent networks of Expressive Leaky Memory neurons. Across auditory and event-based visual classification tasks, receptive fields aligned with task-relevant sensory coordinates improve test accuracy compared to budget-matched random fields, but this advantage disappears when coordinates are scrambled or irrelevant. The benefit diminishes as neuronal expressivity increases, and generic synaptic sparsity regularization only partially recovers performance, indicating that structured receptive fields act as a computational prior beyond sparsity alone.
By Agnese Adorante, Aaron Spieler, Anna Levina
The paper proposes a method to train efficient multi‑task manipulation policies by distilling knowledge from single‑task Conditional Flow Matching (CFM) experts. Instead of training separate models for each task, the authors transfer the experts’ learned velocity fields into a shared policy, combining this distillation signal with the original CFM objective. Experiments on RLBench demonstrate that this approach improves multi‑task performance while keeping the model size fixed, avoiding the need for larger capacity or performance drops seen with naive concatenated training.
By Shreya Deshmukh, Imen Mahdi, Nick Heppert, Abhinav Valada
The paper introduces Newton-Schulz Attention (NS-Attn.), a parameter‑free transformation that applies a finite Newton‑Schulz polynomial step to the output of each attention head in Vision Transformers. By normalizing each head’s feature‑by‑token matrix with its Frobenius norm, applying the NS step, and restoring the norm, the method aims to reduce spectral concentration and increase effective rank before merging heads. Experiments on ViT and Swin models over CIFAR‑10 and CIFAR‑100 show consistent accuracy gains of 0.25–0.83 percentage points, though with added inference latency.
By Xiaohe Jiang (University of Exeter), Guoqiang Zhang (University of Exeter), Tianjin Huang (University of Exeter), Ronghui Mu (University of Exeter)
arXiv:2609.27988v1 Announce Type: cross
Abstract: Methods operating on Vision Transformer (ViT) feature spaces typically rely on Euclidean distance or cosine similarity. This assumes that every direc...
By Andrew Bond, Ege Erdem \"Ozl\"u, Tuna \c{C}imen, Ilkin Umut Melanlioglu, Tolga Birdal, Erkut Erdem, Aykut Erdem
arXiv:2609.27284v1 Announce Type: new
Abstract: We present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but ac...
By Tencent Hunyuan Team, Ao Liu, Botong Zhou, Can Xu, Chayse Zhou, ChenChen Zhang, Chengcheng Xu, Chenhao Wang, Decheng Wu, Dengpeng Wu, Dian Jiao, Dong Du, Dong Wang, Feng Zhang, Fengzong Lian, Guanghui Xu, Guanwei Zhang, Hai Wang, Haipeng Luo, Han Hu, Huilin Xu, Jiajia Wu, Jianchen Zhu, Jianfeng Yan, Jiaqi Zhu, Jihong Zhang, Jinbao Xue, Jun Xia, Junqiang Zheng, Kai Liu, Kai Zhang, Kai Zheng, Kejiao Li, Keyao Wang, Lan Jiang, Lixin Liu, Lulu Wu, Mengyuan Huang, Peijie Yu, Peiqi Wang, Qian Wang, Qianbiao Xiang, Qibin Liu, Qingfeng Sun, Richard Guo, Ruobing Xie, Saiyong Yang, Shaohua Chen, Shihui Hu, Shuai Li, Shuaipeng Li, Shuang Chen, Suncong Zheng, Tao Yang, Tian Zhang, Tinghao Yu, Weidong Han, Weijie Liu, Weijin Zhou, Weikang Wang, Wesleye Chen, Xiao Feng, Xiaoqin Ren, Xingwu Sun, Xiong Kuang, Xuemeng Huang, Xun Cao, Yanfeng Chen, Yang Du, Zhen Yang, Yangyu Tao, Yaping Deng, Yi Shen, Yigeng Hong, Yiqi Chen
arXiv:2609.26820v1 Announce Type: cross
Abstract: Physiological time series such as electrocardiograms (ECG) and electroencephalograms (EEG) exhibit complex temporal structure, substantial acquisitio...
By Naser Mansour, Sidahmed Benabderrahmane, Ameer Rahwan
arXiv:2609.27040v1 Announce Type: cross
Abstract: Multi-GPU servers have become the standard building block of modern data centers, providing aggregated capacity through high-bandwidth interconnects....
By Yi Xu, Tian Xia, Ion Stoica
arXiv:2609.27355v1 Announce Type: cross
Abstract: Unlearning ensures LLM compliance by removing the influence of private or copyrighted training data. However, since LLM models typically undergo post...
By Jialu Wang, Jianing Deng, Shuqing Luo, Yuanzhe Li, Dongwei Wang, Jingtong Hu, Huanrui Yang, Song Wang, Tianlong Chen
arXiv:2609.27378v1 Announce Type: cross
Abstract: End-to-end speech-to-speech dialogue models listen and speak simultaneously, so a continuously open acoustic channel is exposed to adversarial manipu...
By Kian Shamsaie, Iman Modarressi
arXiv:2609.27868v1 Announce Type: cross
Abstract: Octree-based 3D Gaussian Splatting organizes anchors into multi-level hierarchies for level-of-detail rendering, but features at different levels are...
By Wei Zhang, Shiqiang Gong, Shengkai Yu, Zeyu Wang, Clement Mallet, Zhitong Xiong, Qi Wang
arXiv:2609.28358v1 Announce Type: cross
Abstract: Microscaling quantization techniques are increasingly used to represent neural network parameters with 8 bits or fewer while preserving near-full pre...
By Romain Facq, Sami Ben Ali, Olivier Sentieys
arXiv:2609.27421v1 Announce Type: new
Abstract: Multi-constraint instruction following requires a model to respond to a query under many simultaneously active constraints. Even strong instruction-tun...
By Yanzhao Zheng, Yuanqiang Yu, Tianze Xu, Chao Ma, Zhentao Zhang, Jihuai Zhu, Baohua Dong, Hangcheng Zhu, Ruohui Huang
arXiv:2609.27076v1 Announce Type: new
Abstract: Open-vocabulary visual grounding enables robots to localise task-relevant entities from natural-language queries without dependence on predefined perce...
By Linus Nwankwo, Muslim Alaran, Christian Rauch, Stanley Chukwuebuka Obilikpa, Elmar Rueckert
arXiv:2609.27233v1 Announce Type: new
Abstract: Transformer language models process sequences token by token in an autoregressive manner, making growing contexts increasingly expensive. Yet many adja...
By Zixuan Lan, Jessica Yang, Yanhong Li, Karen Livescu, Jiawei Zhou
Few-step autoregressive (AR) video diffusion enables low-latency streaming generation, but existing post-training methods predominantly rely on Distribution Matching Distillation (DMD), requiring both...
Protein modification requires navigating an immense sequence space, yet wet-lab validation remains low-throughput and costly. Although computational paradigms including protein language models (PLMs),...
Deploying deep learning models on edge CPUs is bottlenecked by computational and memory constraints. Mixed-precision quantization promises to reduce inference latency while preserving accuracy. Howeve...
Segmentation-based multi-object tracking (MOT) with foundation video models such as SAM2 offers strong localization quality, yet remains fragile in crowded, real-world scenes. In detector-prompted SAM...