arXiv:2604.18746v1 Announce Type: new Abstract: Capacitated Vertex Cover is the hard-capacitated variant of Vertex Cover: given a graph, a capacity for every vertex, and an integer $k$, the task is to select at most $k$ vertices that cover all edges and assign each edge to one of its chosen endpoints so that no chosen vertex receives more incident edges than its capacity. This problem is a classical benchmark in parameterized complexity, as it was among the first natural […]

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arXiv:2601.20071v1 Announce Type: new Abstract: Gradient-regularized value learning methods improve sample efficiency by leveraging learned models of transition dynamics and rewards to estimate return gradients. However, existing approaches, such as MAGE, struggle in stochastic or noisy environments, limiting their applicability. In this work, we address these limitations by extending distributional reinforcement learning on continuous state-action spaces to model not only the distribution over scalar state-action value functions but also over their gradients. We refer to this approach as […]

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arXiv:2601.04263v1 Announce Type: new Abstract: Knowledge distillation has proven effective for model compression by transferring knowledge from a larger network called the teacher to a smaller network called the student. Current knowledge distillation in time series is predominantly based on logit and feature aligning techniques originally developed for computer vision tasks. These methods do not explicitly account for temporal data and fall short in two key aspects. First, the mechanisms by which the transferred knowledge helps the student […]

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Although it is not always immediately obvious to higher-ups, employee disengagement can quickly have a huge impact on a wider team within an organisation if it is not addressed. Even in large companies where employees often have a higher tenure than in smaller businesses due to the stability and growth prospects, the large scale of the workforce means that small issues can blow up into significant problems. Once disengagement starts to spread, company culture and productivity can take […]

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LiteLLM Hack: Were You One of the 47,000? Daniel Hnyk used the BigQuery PyPI dataset to determine how many downloads there were of the exploited LiteLLM packages during the 46 minute period they were live on PyPI. The answer was 46,996 across the two compromised release versions (1.82.7 and 1.82.8). They also identified 2,337 packages that depended on LiteLLM – 88% of which did not pin versions in a way that would have avoided the exploited version. Via […]

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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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Overview We have recently built and launched our multi-seller SaaS B2B platform with Stripe Connect. Our platform is intended for small businesses with mainly offline presence to easily start selling gift cards online. Stripe Connect Express makes it possible to build a multi-seller platform surprisingly quickly. You can onboard sellers, take application fees, and route payments directly to third-party accounts with relatively little code. Stripe can handle compliance, payouts, and much of the operational complexity. However, in practice, […]

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The impossibility of simultaneously cloning non-orthogonal states lies at the foundations of quantum theory. Even when allowing for approximation errors, cloning an arbitrary unknown pure state requires as many initial copies as needed to fully learn the state. Rather than arbitrary unknown states, modern quantum learning theory often considers structured classes of states and exploits such structure to develop learning algorithms that outperform general-state tomography. This raises the question: How do the sample complexities of learning and cloning […]

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arXiv:2601.05417v1 Announce Type: new Abstract: We present a novel framework for analyzing blockchain consensus mechanisms by modeling blockchain growth as a Partially Observable Stochastic Game (POSG) which we reduce to a set of Partially Observable Markov Decision Processes (POMDPs) through the use of the mean field approximation. This approach formalizes the decision-making process of miners in Proof-of-Work (PoW) systems and enables a principled examination of block selection strategies as well as steady state analysis of the induced Markov […]

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arXiv:2603.15723v1 Announce Type: new Abstract: Large language models are increasingly deployed in settings where relevant information is embedded within long and noisy contexts. Despite this, robustness to growing context length remains poorly understood across different question answering tasks. In this work, we present a controlled empirical study of context-length robustness in large language models using two widely used benchmarks: SQuAD and HotpotQA. We evaluate model accuracy as a function of total context length by systematically increasing the amount […]

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