arXiv:2604.26834v1 Announce Type: cross
Abstract: We present a quantum feature-selection framework based on a higher-order unconstrained binary optimization (HUBO) formulation that explicitly incorporates multivariate dependencies beyond standard quad…
arXiv:2604.26571v1 Announce Type: new
Abstract: Municipal solid waste incineration is increasingly central to urban waste management, yet its sustainability benefit depends on controlling carbon emissions and multiple air pollutants under highly heter…
arXiv:2604.26807v1 Announce Type: new
Abstract: Despite being resource-intensive to train, 3D convolutional neural networks (CNNs) have been the standard approach to classify CT and MRI scans. Recent work suggests that deep multiple instance learning …
arXiv:2604.26837v1 Announce Type: new
Abstract: Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing only a small, query-dependent subset of the KV state p…
arXiv:2604.26197v1 Announce Type: cross
Abstract: Large Language Model (LLM) agents are increasingly used in real-world products, where personalized and context-aware user interactions are essential. A central enabler of such capabilities is the agent…
arXiv:2603.25342v2 Announce Type: replace
Abstract: Deep Research Agents (DRAs) aim to answer complex questions by searching the web, checking evidence, and synthesizing conclusions across heterogeneous sources. We introduce a category-theoretic frame…
arXiv:2604.23758v2 Announce Type: replace
Abstract: The discovery of novel materials is critical for global energy and quantum technology transitions. While deep learning has fundamentally reshaped this landscape, existing predictive or generative mod…
arXiv:2604.26566v1 Announce Type: cross
Abstract: Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumption, and competition for shared charging infrastruct…
arXiv:2604.25942v1 Announce Type: new
Abstract: Left ventricular ejection fraction (LVEF) assessment depends on echocardiography, limiting access in primary care and resource-constrained settings. We developed a multimodal machine-learning framework t…
arXiv:2604.26181v1 Announce Type: new
Abstract: Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input complexity, and available platform resources. Current n…