arXiv:2602.23469v1 Announce Type: new Abstract: There is a growing demand for supporting inference queries that combine Structured Query Language (SQL) and Artificial Intelligence / Machine Learning (AI/ML) model inferences in database systems, to avoid data denormalization and transfer, facilitate management, and alleviate privacy concerns. Co-optimization techniques for executing inference queries in database systems without accuracy loss fall into four categories: (O1) Relational algebra optimization treating AI/ML models as black-box user-defined functions (UDFs); (O2) Factorized AI/ML inferences; (O3) Tensor-relational […]

Ver mais

Hey Hackers! This article is the second in a two-part series on the engineering reality of Physical AI. In Part 1, titled The Next Great Engineering Frontier: The Hidden Complexity of Physical AI, I discussed how the chaotic physical world, the high stakes of failure, and the Sim-to-Real gap make using embodied AI in robotics a harder problem than generating text, images, or video via an AI chatbot. However, mastering the physics is only half the battle. The […]

Ver mais

Reinforcement Learning (RL) is essential for evolving Large Language Models (LLMs) into autonomous agents capable of long-horizon planning, yet a practical recipe for scaling RL in complex, multi-turn environments remains elusive. This paper presents a systematic empirical study using TravelPlanner, a challenging testbed requiring tool orchestration to satisfy multifaceted constraints. We decompose the agentic RL design space along 5 axes: reward shaping, model scaling, data composition, algorithm selection, and environmental stability. Our controlled experiments yield 7 key takeaways, […]

Ver mais

arXiv:2601.10494v1 Announce Type: new Abstract: With grid operators confronting rising uncertainty from renewable integration and a broader push toward electrification, Demand-Side Management (DSM) — particularly Demand Response (DR) — has attracted significant attention as a cost-effective mechanism for balancing modern electricity systems. Unprecedented volumes of consumption data from a continuing global deployment of smart meters enable consumer segmentation based on real usage behaviours, promising to inform the design of more effective DSM and DR programs. However, existing clustering-based […]

Ver mais

arXiv:2603.00646v1 Announce Type: new Abstract: Agent-native social platforms such as Moltbook are rapidly emerging, yet they inherit and amplify classical influence and abuse attacks, where coordinated agents strategically comment and upvote to manipulate visibility and propagate narratives across communities. However, rigorous measurement and learning-based monitoring remain constrained by the absence of longitudinal, graph-native datasets for agentic social networks that jointly capture heterogeneous interactions, temporal drift, and visibility signals needed to connect coordination behavior to downstream exposure. We introduce […]

Ver mais

Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

Ver mais

Cyber-physical systems (CPS) in safety-critical domains, including autonomous driving and robotic surgery, high-speed railways and power grids, increasingly rely on reinforcement learning (RL) as a method for decision-making through time. Unfortunately, deep RL policies are extremely brittle to adversarial perturbations; small, carefully crafted alterations to a policy’s observations or dynamics can result in catastrophic failure. Existing adversarial training methods mainly address static perception tasks and miss the nature of expected temporal compounding of perturbations under hard safety constraints […]

Ver mais