arXiv:2601.17025v1 Announce Type: new Abstract: Among populations with limited literacy in emerging digital markets, the adoption of mobile phones, combined with comprehension barriers and poor cybersecurity hygiene, has created hidden privacy risks. This paper examines how informed consent is often abused by predatory financial applications, leading to financial scams that disproportionately affect users with low literacy. We focus on predatory loan, gambling, and trading apps, analyzing a dataset of 50 Google Play Store apps to measure how many […]

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Every AI-powered iOS app starts with the same assumption: “We’ll just call the AI API and show the result.” That assumption works — right up until real users, real devices, and real constraints enter the system. AI demos look simple. Production AI on iOS is not. This article is about what breaks when AI is treated as a regular API call — and what iOS engineers inevitably learn when shipping AI-powered features to real users. The Mental Model […]

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Although the Twilight movie franchise ended over 10 years ago, it has remained seeded in people’s hearts. It had it all: vampires, werewolves, a love triangle, and a really distracting CGI baby. It was just a different time, really, and you had to be there to fully experience the Twilight craze. But for those who couldn’t, this is the next best thing. Here are all 5 Twilight movies in chronological order. So, the next weekend you have free, […]

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Continual learning is a challenge for models with static architecture, as they fail to adapt to when data distributions evolve across tasks. We introduce a mathematical framework that jointly models architecture and weights in a Sobolev space, enabling a rigorous investigation into the role of neural network architecture in continual learning and its effect on the forgetting loss. We derive necessary conditions for the continual learning solution and prove that learning only model weights is insufficient to mitigate […]

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Reinforcement learning has recently gained traction as a means to improve combinatorial optimization methods, yet its effectiveness within local search metaheuristics specifically remains comparatively underexamined. In this study, we evaluate a range of reinforcement learning-based neighborhood selection strategies — multi-armed bandits (upper confidence bound, $ε$-greedy) and deep reinforcement learning methods (proximal policy optimization, double deep $Q$-network) — and compare them against multiple baselines across three different problems: the traveling salesman problem, the pickup and delivery problem with time […]

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arXiv:2601.03306v1 Announce Type: new Abstract: The game of Go has long served as a benchmark for artificial intelligence, demanding sophisticated strategic reasoning and long-term planning. Previous approaches such as AlphaGo and its successors, have predominantly relied on model-based Monte-Carlo Tree Search (MCTS). In this work, we present QZero, a novel model-free reinforcement learning algorithm that forgoes search during training and learns a Nash equilibrium policy through self-play and off-policy experience replay. Built upon entropy-regularized Q-learning, QZero utilizes a […]

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arXiv:2512.20762v2 Announce Type: replace-cross Abstract: We study the problem of subgroup discovery for survival analysis, where the goal is to find an interpretable subset of the data on which a Cox model is highly accurate. Our work is the first to study this particular subgroup problem, for which we make several contributions. Subgroup discovery methods generally require a “quality function” in order to sift through and select the most advantageous subgroups. We first examine why existing natural choices […]

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Just read through this new systematic review (arXiv:2601.09334) on Serverless for HPC/AI. It’s a solid read if you’re dealing with infrastructure scaling. The TL;DR: Static Allocation is breaking: The paper argues that rigid GPU clusters can’t handle modern “bursty” AI workloads efficiently. You either over-provision (waste money) or under-provision (crash during spikes). Serverless is the fix: The industry is moving toward elastic, serverless execution models to survive the efficiency gap. We’ve been seeing this exact pattern in production. […]

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Autonomous mission planning for Active Debris Removal (ADR) must balance efficiency, adaptability, and strict feasibility constraints on fuel and mission duration. This work compares three planners for the constrained multi-debris rendezvous problem in Low Earth Orbit: a nominal Masked Proximal Policy Optimization (PPO) policy trained under fixed mission parameters, a domain-randomized Masked PPO policy trained across varying mission constraints for improved robustness, and a plain Monte Carlo Tree Search (MCTS) baseline. Evaluations are conducted in a high-fidelity orbital […]

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arXiv:2601.08967v1 Announce Type: new Abstract: The classical Maker-Breaker positional game is played on a board which is a hypergraph $mathcal{H}$, with two players, Maker and Breaker, alternately claiming vertices of $mathcal{H}$ until all the vertices are claimed. When the game ends, Maker wins if she has claimed all the vertices of some edge of $mathcal{H}$; otherwise, Breaker wins. Playing this game in real life can be done by placing tokens on the vertices of the board. In this […]

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