arXiv:2602.12413v1 Announce Type: new Abstract: If LLM training data is polluted with benchmark test data, then benchmark performance gives biased estimates of out-of-distribution (OOD) generalization. Typical decontamination filters use n-gram matching which fail to detect semantic duplicates: sentences with equivalent (or near-equivalent) content that are not close in string space. We study this soft contamination of training data by semantic duplicates. Among other experiments, we embed the Olmo3 training corpus and find that: 1) contamination remains widespread, e.g. […]

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arXiv:2603.00663v1 Announce Type: new Abstract: The Moving Target Vehicle Routing Problem (MT-VRP) seeks trajectories for several agents that intercept a set of moving targets, subject to speed, time window, and capacity constraints. We introduce an exact algorithm, Branch-and-Price with Relaxed Continuity (BPRC), for the MT-VRP. The main challenge in a branch-and-price approach for the MT-VRP is the pricing subproblem, which is complicated by moving targets and time-dependent travel costs between targets. Our key contribution is a new labeling […]

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arXiv:2603.24815v1 Announce Type: new Abstract: Pin sites represent the interface where a metal pin or wire from the external environment passes through the skin into the internal environment of the limb. These pins or wires connect an external fixator to the bone to stabilize the bone segments in a patient with trauma or deformity. Because these pin sites represent an opportunity for external skin flora to enter the internal environment of the limb, infections of the pin site […]

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Remote work managers are extremely stressed in 2026… but why? As the keepers of productivity, it’s important to know the team is working but not feeling watched. Monitoring software, screenshots every ten minutes, activity percentages, app usage logs, have been the answer in the past. What happened? Employees felt surveilled, trust eroded quietly, and workplace dynamics became parole, not a partnership. But, what if all that tracking data was ineffective? How Do People Really Work? Time tracking has […]

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Standard supervised training for deepfake detection treats all samples with uniform importance, which can be suboptimal for learning robust and generalizable features. In this work, we propose a novel Tutor-Student Reinforcement Learning (TSRL) framework to dynamically optimize the training curriculum. Our method models the training process as a Markov Decision Process where a “Tutor” agent learns to guide a “Student” (the deepfake detector). The Tutor, implemented as a Proximal Policy Optimization (PPO) agent, observes a rich state representation […]

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This paper, which is entirely unconditional, proves a sharpened almost-all theorem with fully explicit effective constants for the restricted weighted Goldbach sum R_{a,q}(N) := sum over p1+p2=N, p1 = a (mod q), of (log p1)(log p2), with q >= 1 and gcd(a,q) = 1, whose expected main term is M_{a,q}(N) = C_2 * S(N) * N / phi(q), where C_2 = 0.6601618… is the twin-prime constant and S(N) is the binary singular series.The results are organised around four […]

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We propose a federated learning framework for the calibration of parametric insurance indices under heterogeneous renewable energy production losses. Producers locally model their losses using Tweedie generalized linear models and private data, while a common index is learned through federated optimization without sharing raw observations. The approach accommodates heterogeneity in variance and link functions and directly minimizes a global deviance objective in a distributed setting. We implement and compare FedAvg, FedProx and FedOpt, and benchmark them against an […]

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arXiv:2510.26303v3 Announce Type: replace-cross Abstract: Adam [Kingma & Ba, 2015] is the de facto optimizer in deep learning, yet its theoretical understanding remains limited. Prior analyses show that Adam favors solutions aligned with $ell_infty$-geometry, but these results are restricted to the full-batch regime. In this work, we study the implicit bias of incremental Adam (using one sample per step) for logistic regression on linearly separable data, and show that its bias can deviate from the full-batch behavior. As […]

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