Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using a flow and a retrieval network. Despite their demonstrated efficiency compared to state-of-the-art Reinforcement Learning (RL) algorithms, their practical application in robotic control tasks is constrained by the reliance on pre-training the retrieval network. This dependency poses challenges in dynamic robotic environments, where pre-training data may not be readily available or representative of the current environment. This paper […]

Ver mais

arXiv:2601.08000v1 Announce Type: new Abstract: Ensuring that Large Language Models (LLMs) adhere to safety principles without refusing benign requests remains a significant challenge. While OpenAI introduces deliberative alignment (DA) to enhance the safety of its o-series models through reasoning over detailed “code-like” safety rules, the effectiveness of this approach in open-source LLMs, which typically lack advanced reasoning capabilities, is understudied. In this work, we systematically evaluate the impact of explicitly specifying extensive safety codes versus demonstrating them through […]

Ver mais

Victoria, Seychelles, February 5, 2026 MEXC, the fastest-growing global cryptocurrency exchange, redefining a user-first approach to digital assets through true zero-fee trading, today reported a 65% surge in January spot trading volume of newly listed tokens, as infrastructure projects—AI agents, zero-knowledge networks, and decentralized physical systems—captured the majority of sustained capital flows. n January’s 176 token listings drove spot trading volume in newly listed tokens up 65% and attracted 19% more active traders. The top performers averaged up […]

Ver mais

arXiv:2309.16858v5 Announce Type: replace Abstract: We introduce Transductive Local Complexity (TLC) to extend the classical Local Rademacher Complexity (LRC) to the transductive setting, incorporating substantial and novel components. Although LRC has been used to obtain sharp generalization bounds and minimax rates for inductive tasks such as classification and nonparametric regression, it has remained an open problem whether a localized Rademacher complexity framework can be effectively adapted to transductive learning to achieve sharp or nearly sharp bounds consistent with […]

Ver mais

The Fear I like AI and have been working with the technology for over a decade. It’s been exciting, interesting, and inspiring — until recently. As I write this in mid-February of 2026, software stocks are in freefall and social media is inundated with videos of AI company CEOs predicting the looming collapse of the software industry. They tell us the story we’ve heard on loop for two years: “Most white-collar work will be fully automated within a […]

Ver mais

arXiv:2604.07514v1 Announce Type: new Abstract: Drone delivery is rapidly emerging as a cost-effective and energy efficient alternative for last-mile delivery. Unlike ground vehicles, a drone’s energy consumption depends on its payload in addition to travel distance. This creates a unique environmental challenge for multi-stop delivery tours, as the drone’s total weight, and therefore its energy consumption rate, dynamically changes after each delivery. This paper investigates a novel green drone routing problem focused on maximizing energy efficiency. Through a […]

Ver mais

arXiv:2601.09018v1 Announce Type: new Abstract: Across engineering and scientific domains, traditional deep learning (TDL) models perform well when training and test data share the same distribution. However, the dynamic nature of real-world data, broadly termed textit{data shift}, renders TDL models prone to rapid performance degradation, requiring costly relabeling and inefficient retraining. Meta-learning, which enables models to adapt quickly to new data with few examples, offers a promising alternative for mitigating these challenges. Here, we systematically compare TDL with […]

Ver mais

If I see a Variable that doesn’t change. I call that variable a constant TL;DR: Be explicit on what mutates and what doesn’t. Problems Addressed 😔 Mutability Code Optimization Related Code Smells 💨 https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxxii?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxvi?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxiv?embedable=true Context 💬 Variables that never change their value are not really variables. They’re constants pretending to be mutable state. When we declare something as a variable, we tell the reader (and the compiler) to expect change. If that change never happens, we’re […]

Ver mais

arXiv:2601.00192v1 Announce Type: new Abstract: Cardiovascular diseases, particularly arrhythmias, remain a leading global cause of mortality, necessitating continuous monitoring via the Internet of Medical Things (IoMT). However, state-of-the-art deep learning approaches often impose prohibitive computational overheads, rendering them unsuitable for resource-constrained edge devices. This study proposes a resource-efficient, data-centric framework that prioritizes feature engineering over complexity. Our optimized pipeline makes the complex, high-dimensional arrhythmia data linearly separable. This is achieved by integrating time-frequency wavelet decompositions with graph-theoretic structural […]

Ver mais

arXiv:2306.06581v2 Announce Type: replace Abstract: Sinkhorn algorithm has been used pervasively to approximate the solution to optimal transport (OT) and unbalanced optimal transport (UOT) problems. However, its practical application is limited due to the high computational complexity. To alleviate the computational burden, we propose a novel importance sparsification method, called Spar-Sink, to efficiently approximate entropy-regularized OT and UOT solutions. Specifically, our method employs natural upper bounds for unknown optimal transport plans to establish effective sampling probabilities, and constructs […]

Ver mais