arXiv:2405.15314v5 Announce Type: replace Abstract: Incorporating domain-specific constraints into machine learning models is essential for generating predictions that are both accurate and feasible in real-world applications. This paper introduces new methods for training Output-Constrained Regression Trees (OCRT), addressing the limitations…
arXiv:2604.02344v1 Announce Type: new Abstract: WebGPU’s security-focused design imposes per-operation validation that compounds across the many small dispatches in neural network inference, yet the true cost of this overhead is poorly characterized. We present a systematic characterization of WebGPU dispatch…
arXiv:2509.21716v2 Announce Type: replace Abstract: Harnessing parallelism in seemingly sequential models is a central challenge for modern machine learning. Several approaches have been proposed for evaluating sequential processes in parallel using iterative fixed-point methods, like Newton, Picard, and Jacobi iterations.…
arXiv:2604.02345v1 Announce Type: new Abstract: Scaling generalist GUI agents is hindered by the data scalability bottleneck of expensive human demonstrations and the “distillation ceiling” of synthetic teacher supervision. To transcend these limitations, we propose UI-Oceanus, a framework that shifts the…
arXiv:2604.02889v1 Announce Type: cross Abstract: Data assimilation is the process of estimating the time-evolving state of a dynamical system by integrating model predictions and noisy observations. It is commonly formulated as Bayesian filtering, but classical filters often struggle with accuracy…
arXiv:2604.03086v1 Announce Type: cross Abstract: This work establishes a rigorous bridge between infinite-dimensional delay dynamics and finite-dimensional Koopman learning, with explicit and interpretable error guarantees. While Koopman analysis is well-developed for ordinary differential equations (ODEs) and partially for partial differential…
arXiv:2511.14617v3 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has emerged as a critical technique for advancing modern Large Language Models (LLMs), yet existing synchronous RL systems face severe performance bottlenecks. The rollout phase, which dominates end-to-end iteration time, suffers from…
arXiv:2604.02337v1 Announce Type: new Abstract: Counterfactual simulation – exploring hypothetical consequences under alternative clinical scenarios – holds promise for transformative applications such as personalized medicine and in silico trials. However, it remains challenging due to methodological limitations. Here, we show…
arXiv:2602.18523v3 Announce Type: replace Abstract: Grokking — the abrupt transition from memorization to generalization long after near-zero training loss — has been studied mainly in single-task settings. We extend geometric analysis to multi-task modular arithmetic, training shared-trunk Transformers on dual-task…
arXiv:2503.02178v2 Announce Type: replace-cross Abstract: This paper develops asymptotic theory for quantile estimation via stochastic gradient descent (SGD) with a constant learning rate. The quantile loss function is neither smooth nor strongly convex. Beyond conventional perspectives and techniques, we view…
