Tootfinder

Long-term unsupervised recalibration of cursor-based intracortical brain–computer interfaces using a hidden Markov model #BCI

Long-term unsupervised recalibration of cursor-based intracortical brain–computer interfaces using a hidden Markov model - Nature Biomedical Engineering
A hidden Markov model that uses probabilistic retrospective inference allows for up to one month of unsupervised recalibration in an online cursor-based intracortical brain–machine interface.

Inference Attacks on Encrypted Online Voting via Traffic Analysis
Anastasiia Belousova, Francesco Marchiori, Mauro Conti
https://arxiv.org/abs/2509.15694 https://

Inference Attacks on Encrypted Online Voting via Traffic Analysis
Online voting enables individuals to participate in elections remotely, offering greater efficiency and accessibility in both governmental and organizational settings. As this method gains popularity, ensuring the security of online voting systems becomes increasingly vital, as the systems supporting it must satisfy a demanding set of security requirements. Most research in this area emphasizes the design and verification of cryptographic protocols to protect voter integrity and system confiden…

Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training
Wei Xiong, Chenlu Ye, Baohao Liao, Hanze Dong, Xinxing Xu, Christof Monz, Jiang Bian, Nan Jiang, Tong Zhang
https://arxiv.org/abs/2510.04996

Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training
Reinforcement learning applied to large language models (LLMs) for reasoning tasks is often bottlenecked by unstable gradient estimates due to fixed and uniform sampling of responses across prompts. Prior work such as GVM-RAFT addresses this by dynamically allocating inference budget per prompt to minimize stochastic gradient variance under a budget constraint. Inspired by this insight, we propose Reinforce-Ada, an adaptive sampling framework for online RL post-training of LLMs that continuousl…

Robust, Online, and Adaptive Decentralized Gaussian Processes
Fernando Llorente, Daniel Waxman, Sanket Jantre, Nathan M. Urban, Susan E. Minkoff
https://arxiv.org/abs/2509.18011

Robust, Online, and Adaptive Decentralized Gaussian Processes
Gaussian processes (GPs) offer a flexible, uncertainty-aware framework for modeling complex signals, but scale cubically with data, assume static targets, and are brittle to outliers, limiting their applicability in large-scale problems with dynamic and noisy environments. Recent work introduced decentralized random Fourier feature Gaussian processes (DRFGP), an online and distributed algorithm that casts GPs in an information-filter form, enabling exact sequential inference and fully distribut…

I like to play around as an anonymous commenter in online newspaper columns. If you point out the biases of #AI systems, the comment gets deleted because it is considered too polemical.
The comment was addressing an article about AI in public service and the use in refugee applications 🫣

Automated Multi-Agent Workflows for RTL Design
Amulya Bhattaram, Janani Ramamoorthy, Ranit Gupta, Diana Marculescu, Dimitrios Stamoulis
https://arxiv.org/abs/2509.20182 https://…

Automated Multi-Agent Workflows for RTL Design
The rise of agentic AI workflows unlocks novel opportunities for computer systems design and optimization. However, for specialized domains such as program synthesis, the relative scarcity of HDL and proprietary EDA resources online compared to more common programming tasks introduces challenges, often necessitating task-specific fine-tuning, high inference costs, and manually-crafted agent orchestration. In this work, we present VeriMaAS, a multi-agent framework designed to automatically compo…

Closing the Loop Inside Neural Networks: Causality-Guided Layer Adaptation for Fault Recovery Control
Mahdi Taheri, Soon-Jo Chung, Fred Y. Hadaegh
https://arxiv.org/abs/2509.16837

Closing the Loop Inside Neural Networks: Causality-Guided Layer Adaptation for Fault Recovery Control
This paper studies the problem of real-time fault recovery control for nonlinear control-affine systems subject to actuator loss of effectiveness faults and external disturbances. We derive a two-stage framework that combines causal inference with selective online adaptation to achieve an effective learning-based recovery control method. In the offline phase, we develop a causal layer attribution technique based on the average causal effect (ACE) to evaluate the relative importance of each laye…