On Surjectivity of Neural Networks: Can you elicit any behavior from your model? arXiv:2508.19445v1 Announce Type: new Abstract: Given a trained neural network, can any specified output be generated by some input? Equivalently, does the network correspond to a function…
Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement arXiv:2508.18765v2 Announce Type: replace Abstract: As AI systems evolve into distributed ecosystems with autonomous execution, asynchronous reasoning, and multi-agent coordination, the absence of scalable, decoupled governance poses a structural…
The Sample Complexity of Membership Inference and Privacy Auditing arXiv:2508.19458v1 Announce Type: new Abstract: A membership-inference attack gets the output of a learning algorithm, and a target individual, and tries to determine whether this individual is a member of the…
From Optimization to Control: Quasi Policy Iteration arXiv:2311.11166v3 Announce Type: replace-cross Abstract: Recent control algorithms for Markov decision processes (MDPs) have been designed using an implicit analogy with well-established optimization algorithms. In this paper, we adopt the quasi-Newton method (QNM)…
Incentivized Lipschitz Bandits arXiv:2508.19466v1 Announce Type: new Abstract: We study incentivized exploration in multi-armed bandit (MAB) settings with infinitely many arms modeled as elements in continuous metric spaces. Unlike classical bandit models, we consider scenarios where the decision-maker (principal) incentivizes…
X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models arXiv:2412.01824v2 Announce Type: replace-cross Abstract: In-context generation is a key component of large language models’ (LLMs) open-task generalization capability. By leveraging a few examples as context, LLMs can…
DeepAtlas: a tool for effective manifold learning arXiv:2508.19479v1 Announce Type: new Abstract: Manifold learning builds on the “manifold hypothesis,” which posits that data in high-dimensional datasets are drawn from lower-dimensional manifolds. Current tools generate global embeddings of data, rather than…
Predicting Forced Responses of Probability Distributions via the Fluctuation-Dissipation Theorem and Generative Modeling arXiv:2504.13333v2 Announce Type: replace-cross Abstract: We present a novel and flexible data-driven framework for estimating the response of higher-order moments of nonlinear stochastic systems to small external…
Distribution Shift Aware Neural Tabular Learning arXiv:2508.19486v1 Announce Type: new Abstract: Tabular learning transforms raw features into optimized spaces for downstream tasks, but its effectiveness deteriorates under distribution shifts between training and testing data. We formalize this challenge as the…
Analyzing Character Representation in Media Content using Multimodal Foundation Model: Effectiveness and Trust arXiv:2506.14799v2 Announce Type: replace-cross Abstract: Recent advances in AI has made automated analysis of complex media content at scale possible while generating actionable insights regarding character representation…
