arXiv:2601.22183v1 Announce Type: new Abstract: Location-based services rely heavily on efficient methods that search for relevant points-of-interest (POIs) near a given location. A k Nearest Neighbor (kNN) query is one such example that finds the k closest POIs from an agent’s location. While most existing techniques focus on retrieving nearby POIs for a single agent, these search heuristics do not translate to many other applications. For example, Aggregate k Nearest Neighbor (AkNN) queries require POIs that are close […]

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arXiv:2601.16777v1 Announce Type: cross Abstract: Under the assumption that data lie on a compact (unknown) manifold without boundary, we derive finite sample bounds for kernel smoothing and its (first and second) derivatives, and we establish asymptotic normality through Berry-Esseen type bounds. Special cases include kernel density estimation, kernel regression and the heat kernel signature. Connections to the graph Laplacian are also discussed.

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arXiv:2602.07057v1 Announce Type: new Abstract: Urban design profoundly impacts public spaces and community engagement. Traditional top-down methods often overlook public input, creating a gap in design aspirations and reality. Recent advancements in digital tools, like City Information Modelling and augmented reality, have enabled a more participatory process involving more stakeholders in urban design. Further, deep learning and latent diffusion models have lowered barriers for design generation, providing even more opportunities for participatory urban design. Combining state-of-the-art latent diffusion […]

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arXiv:2602.22261v1 Announce Type: new Abstract: Large language models have become central to many AI applications, but their growing energy consumption raises serious sustainability concerns. A key limitation in current AI deployments is the reliance on a one-size-fits-all inference strategy where most systems route every request to the same large model, regardless of task complexity, leading to substantial and unnecessary energy waste. To address this issue, we propose a context-aware model switching approach that dynamically selects an appropriate language […]

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Backdoor attacks enable adversaries to embed malicious behavior into machine learning models by poisoning training data with triggers. Researchers focused largely on backdoors in unimodal models. However, the rise of multimodal systems, e.g., vision–language models (VLMs) and multimodal large language models (MLLMs), has significantly increased the attack surface. Multimodal backdoors can exploit cross-modal triggers, representation-level manipulation, instruction-conditioned behaviors, and test-time activation pathways that are not available in unimodal models. Nevertheless, quantifying progress in this field remains challenging due […]

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DeepXube is a free and open-source Python package and command-line tool that seeks to automate the solution of pathfinding problems by using machine learning to learn heuristic functions that guide heuristic search algorithms tailored to deep neural networks (DNNs). DeepXube is comprised of the latest advances in deep reinforcement learning, heuristic search, and formal logic for solving pathfinding problems. This includes limited-horizon Bellman-based learning, hindsight experience replay, batched heuristic search, and specifying goals with answer-set programming. A robust […]

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arXiv:2604.15341v1 Announce Type: new Abstract: Mixed Reality (MR) offers immersive and multimodal opportunities for education but remains difficult for teachers to author without technical expertise. We propose MRGEN, a conceptual framework for LLM-powered authoring tools to support teachers in creating MR learning activities that work on mobile devices (tablets and smartphones). MRGEN articulates three axes: Learning Objectives, MR Modality, and GAI Assistance. To validate our framework, we implemented a prototype based on the open-source MIXAP authoring platform and […]

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arXiv:2602.11223v1 Announce Type: new Abstract: Chronic diseases constitute the principal burden of morbidity, mortality, and healthcare costs worldwide, yet current health systems remain fragmented and predominantly reactive. Patient Medical Digital Twins (PMDTs) offer a paradigm shift: holistic, continuously updated digital counterparts of patients that integrate clinical, genomic, lifestyle, and quality-of-life data. We report early implementations of PMDTs via ontology-driven modeling and federated analytics pilots. Insights from the QUALITOP oncology study and a distributed AI platform confirm both feasibility […]

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arXiv:2206.13405v2 Announce Type: replace-cross Abstract: Robustness is a fundamental pillar of Machine Learning (ML) classifiers, substantially determining their reliability. Methods for assessing classifier robustness are therefore essential. In this work, we address the challenge of evaluating corruption robustness in a way that allows comparability and interpretability on a given dataset. We propose a test data augmentation method that uses a robustness distance $epsilon$ derived from the datasets minimal class separation distance. The resulting MSCR (minimal separation corruption robustness) […]

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The Amazon Nova AI Challenge is back for its second year. As ten selected university teams from around the world gather in Seattle this week for bootcamp (February 2-4), they’re preparing to tackle a real-world challenge in software development: building AI agents that can handle complex coding tasks while maintaining security and reliability. For the first time in an academic competition, participating teams will use Amazon Nova Forge to customize Nova models with access to tools, models and […]

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