Conventional optimization-based metering depends on strict adherence to precomputed schedules, which limits the flexibility required for the stochastic operations of Advanced Air Mobility (AAM). In contrast, multi-agent reinforcement learning (MARL) offers a decentralized, adaptive framework that can better handle uncertainty, required for safe aircraft separation assurance. Despite this advantage, current MARL approaches often overfit to specific airspace structures, limiting their adaptability to new configurations. To improve generalization, we recast the MARL problem in a relative polar state space […]

Ver mais

Hi everyone, I’m a CS student aiming to master Reinforcement Learning (RL) for industry roles and startup building. I’ve designed the following roadmap and would really appreciate feedback from experienced practitioners. My background: Comfortable with Python, NumPy, Pandas Basic ML & Deep Learning knowledge Long-term goal: RL Engineer / Agentic AI systems 🛣️ My RL Roadmap 1️⃣ Foundations Python (OOP, decorators, multiprocessing) Math: Linear Algebra, Probability, Calculus Markov Processes (MDP, Bellman equations) 2️⃣ Classical RL Multi-armed bandits Dynamic […]

Ver mais

arXiv:2601.05375v1 Announce Type: new Abstract: Modern commercial ground vehicles are increasingly equipped with multiple operational modalities (e.g., human driving, advanced driver assistance, remote tele-operation, full autonomy). These often rely on heterogeneous sensing infrastructures and distinct routing algorithms, which can yield misaligned perceptions of the traffic environment and route preferences. While such technologies accelerate the transition toward increasingly intelligent transportation networks, their current deployment fails to avoid challenges associated with selfish routing behavior, in which drivers or automated agents […]

Ver mais

We all knew that this year’s CES would be dominated by AI, and the reality is nothing short of that. Major companies and startups have unveiled some of the most unique AI products at the largest consumer tech conference in Las Vegas. When we talk of AI, Meta is among the names that pops up in everyone’s head, and rightly so. Meta teleprompter brings notes right to your eyes Well, Meta showed up at the CES 2026 event, […]

Ver mais

In this paper we consider a regularization approach to variable selection when the regression function depends nonlinearly on a few input variables. The proposed method is based on a regularized least square estimator penalizing large values of the partial derivatives. An efficient iterative procedure is proposed to solve the underlying variational problem, and its convergence is proved. The empirical properties of the obtained estimator are tested both for prediction and variable selection. The algorithm compares favorably to more […]

Ver mais

arXiv:2601.04252v1 Announce Type: new Abstract: Pull request (PR) review is essential for ensuring software quality, yet automating this task remains challenging due to noisy supervision, limited contextual understanding, and inadequate evaluation metrics. We present Sphinx, a unified framework for LLM-based PR review that addresses these limitations through three key components: (1) a structured data generation pipeline that produces context-rich, semantically grounded review comments by comparing pseudo-modified and merged code; (2) a checklist-based evaluation benchmark that assesses review quality […]

Ver mais

Progressive driver behavior analytics is crucial for improving road safety and mitigating the issues caused by aggressive or inattentive driving. Previous studies have employed machine learning and deep learning techniques, which often result in low feature optimization, thereby compromising both high performance and interpretability. To fill these voids, this paper proposes a hybrid approach to driver behavior analysis that uses a 12,857-row and 18-column data set taken from Kaggle. After applying preprocessing techniques such as label encoding, random […]

Ver mais

Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior and biases from the Internet. Pretrained language models are very powerful and have shown great success in many NLP tasks. However, to safely deploy them for practical real-world applications demands a strong safety control over the model generation process.

Ver mais

arXiv:2601.00251v1 Announce Type: new Abstract: As wireless networks continue to evolve, stringent latency and reliability requirements and highly dynamic channels expose fundamental limitations of gNB-centric massive multiple-input multiple-output (mMIMO) architectures, motivating a rethinking of the user equipment (UE) role. In response, the UE is transitioning from a passive transceiver into an active entity that directly contributes to system-level performance. In this context, this article examines the evolving role of the UE in mMIMO systems during the transition from […]

Ver mais