arXiv:2504.01382v4 Announce Type: replace Abstract: As digitalization and cloud technologies evolve, the web is becoming increasingly important in the modern society. Autonomous web agents based on large language models (LLMs) hold a great potential in work automation. It is therefore…
arXiv:2510.07073v1 Announce Type: new Abstract: Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge. Large language model (LLM)-based code generation has recently shown promise across many domains, but it…
arXiv:2510.07091v1 Announce Type: new Abstract: Enabling LLMs to effectively operate long-horizon task which requires long-term planning and multiple interactions is essential for open-world autonomy. Conventional methods adopt planning with actions where a executable action list would be provided as reference.…
arXiv:2402.09225v4 Announce Type: replace-cross Abstract: This article introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if given data was used during the training of AI/ML models. Specifically, we propose two MINT architectures designed to…
arXiv:2505.11006v3 Announce Type: replace Abstract: We demonstrate how supervised learning can be decomposed into a two-stage procedure, where (1) all model parameters are selected in an unsupervised manner, and (2) the outputs y are added to the model, without changing…
arXiv:2507.05306v2 Announce Type: replace Abstract: We consider the multinomial logistic bandit problem, a variant of where a learner interacts with an environment by selecting actions to maximize expected rewards based on probabilistic feedback from multiple possible outcomes. In the binary…
arXiv:2510.07250v1 Announce Type: cross Abstract: Given a single observation from a Gaussian distribution with unknown mean $theta$, we design computationally efficient procedures that can approximately generate an observation from a different target distribution $Q_{theta}$ uniformly for all $theta$ in a…
arXiv:2208.03761v2 Announce Type: replace Abstract: The neural tangent kernel is a kernel function defined over the parameter distribution of an infinite width neural network. Despite the impracticality of this limit, the neural tangent kernel has allowed for a more direct…
arXiv:2509.07030v2 Announce Type: replace-cross Abstract: The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all parameters can hinder the incorporation of complex structural constraints. We introduce a minimalist Bayesian framework…
arXiv:2510.03569v2 Announce Type: replace-cross Abstract: Generative models for sequential data often struggle with sparsely sampled and high-dimensional trajectories, typically reducing the learning of dynamics to pairwise transitions. We propose Interpolative Multi-Marginal Flow Matching (IMMFM), a framework that learns continuous stochastic…
