arXiv:2601.00023v1 Announce Type: new Abstract: Efficient workload assignment to the workforce is critical in last-mile package delivery systems. In this context, traditional methods of assigning package deliveries to workers based on geographical proximity can be inefficient and surely guide to an unbalanced workload distribution among delivery workers. In this paper, we look at the problem of operational human resources workload balancing in last-mile urban package delivery systems. The idea is to consider the effort workload to optimize the […]

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arXiv:2602.00995v1 Announce Type: new Abstract: Handheld Optical Coherence Tomography Angiography (OCTA) enables noninvasive retinal imaging in uncooperative or pediatric subjects, but is highly susceptible to motion artifacts that severely degrade volumetric image quality. Sudden motion during 3D acquisition can lead to unsampled retinal regions across entire B-scans (cross-sectional slices), resulting in blank bands in en face projections. We propose VAMOS-OCTA, a deep learning framework for inpainting motion-corrupted B-scans using vessel-aware multi-axis supervision. We employ a 2.5D U-Net architecture […]

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hi folks, im a researcher and have a ton of TPU/GPU credits granted for me. Specifically for coding agent RL (preferably front end coding RL). Ive been working on RL rollout stuff (on the scheduling and infrastructure side). Would love to collab with someone who wants to collab and maybe get a paper out for neurips or something ? at the very least do a arxiv release. submitted by /u/vnwarrior [link] [comments]

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Catastrophic forgetting remains a fundamental challenge for neural networks when tasks are trained sequentially. In this work, we reformulate continual learning as a control problem where learning and preservation signals compete within neural activity dynamics. We convert regularization penalties into preservation signals that protect prior-task representations. Learning then proceeds by minimizing the control effort required to integrate new tasks while competing with the preservation of prior tasks. At equilibrium, the neural activities produce weight updates that implicitly encode […]

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arXiv:2409.14123v3 Announce Type: replace Abstract: Although overparameterized models have achieved remarkable practical success, their theoretical properties, particularly their generalization behavior, remain incompletely understood. The well known double descents phenomenon suggests that the test error curve of neural networks decreases monotonically as model size grows and eventually converges to a non-zero constant. This work aims to explain the theoretical mechanism underlying this tail behavior and study the statistical consistency of deep overparameterized neural networks in many different learning tasks […]

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arXiv:2604.05669v1 Announce Type: new Abstract: There is a growing demand for efficient data removal to comply with regulations like the GDPR and to mitigate the influence of biased or corrupted data. This has motivated the field of machine unlearning, which aims to eliminate the influence of specific data subsets without the cost of full retraining. In this work, we propose a statistical framework for machine unlearning with generic loss functions and establish theoretical guarantees. For squared loss, especially, […]

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arXiv:2604.02503v1 Announce Type: new Abstract: This work presents the system identification of a variable-pitch propeller (VPP) powertrain, encompassing the full actuation chain from PWM signals to thrust generation, with the aim of developing compact models suitable for real-time digital twinning and control applications. The identification is grounded in experimental data covering both static and dynamic responses of the system. The proposed model takes the form of a Wiener-like architecture, where the PWM inputs are first processed through linear […]

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Curriculum learning is a class of training strategies that organizes the data being exposed to a model by difficulty, gradually from simpler to more complex examples. This research explores a reverse curriculum generation approach that recursively decomposes complex datasets into simpler, more learnable components. We propose a teacher-student framework where the teacher is equipped with the ability to reason step-by-step, which is used to recursively generate easier versions of examples, enabling the student model to progressively master difficult […]

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Token marketing has undergone a structural transformation. In earlier cycles, exchange listings and social momentum were often sufficient to create price activity and short-term attention. Visibility alone could sustain narrative velocity. In 2026, that model has weakened considerably. Sustainable token growth now depends on structural clarity, regulatory awareness, credible positioning, and ecosystem durability. The conversation has shifted from amplification to architecture. A token is no longer treated as a feature layered onto a product. It functions as an […]

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