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arXiv:2602.11880v1 Announce Type: new Abstract: Defective and inconsistent responses in CT detectors can cause ring and streak artifacts in the reconstructed images, making them unusable...
arXiv:2602.11877v1 Announce Type: new Abstract: Large language models (LLMs) have achieved success, but cost and privacy constraints necessitate deploying smaller models locally while offloading complex...
arXiv:2602.11875v1 Announce Type: new Abstract: Generative models have advanced significantly in realistic image synthesis, with diffusion models excelling in quality and stability. Recent multi-view diffusion...
arXiv:2602.11874v1 Announce Type: new Abstract: Journalistic fact-checking, as well as social or economic research, require analyzing high-quality statistics datasets (SDs, in short). However, retrieving SD...
arXiv:2602.11871v1 Announce Type: new Abstract: Large Language Models (LLMs) are a powerful tool for statistical text analysis, with derived sequences of next-token probability distributions offering...
arXiv:2602.11870v1 Announce Type: new Abstract: We present the novel Reduced Basis Virtual Element Method (rbVEM) for solving the Laplace eigenvalue problem. This approach is based...
arXiv:2602.11865v1 Announce Type: new Abstract: AI agents are able to tackle increasingly complex tasks. To achieve more ambitious goals, AI agents need to be able...
arXiv:2602.11863v1 Announce Type: new Abstract: Large language models (LLMs) can learn from a few demonstrations provided at inference time. We study this in-context learning phenomenon...
arXiv:2602.11862v1 Announce Type: new Abstract: Recent advances in vision-language models have made zero-shot navigation feasible, enabling robots to follow natural language instructions without requiring labeling....
arXiv:2602.11861v1 Announce Type: new Abstract: Building upon recent structural disentanglement frameworks for sign language production, we propose A$^{2}$V-SLP, an alignment-aware variational framework that learns articulator-wise...
arXiv:2602.11860v1 Announce Type: new Abstract: Dynamic maps (DM) serve as the fundamental information infrastructure for vehicle-road-cloud (VRC) cooperative autonomous driving in China and Japan. By...
arXiv:2602.11858v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at broad visual understanding but still struggle with fine-grained perception, where decisive evidence is...
arXiv:2602.11857v1 Announce Type: new Abstract: Scale-invariance in games has recently emerged as a widely valued desirable property. Yet, almost all fast convergence guarantees in learning...
arXiv:2602.11855v1 Announce Type: new Abstract: Discovering technology opportunities (TOD) remains a critical challenge for innovation management, especially in early-stage development where consumer needs are often...
arXiv:2602.11854v1 Announce Type: new Abstract: We study the design of resilient and reliable communication networks in which a signal can be transferred only up to...
arXiv:2602.11852v1 Announce Type: new Abstract: While state-of-the-art language models (LMs) surpass the vast majority of humans in certain domains, their reasoning remains largely opaque, undermining...
arXiv:2602.11851v1 Announce Type: new Abstract: Collaborative Intrusion Detection Systems (CIDS) are increasingly adopted to counter cyberattacks, as their collaborative nature enables them to adapt to...
arXiv:2602.11850v1 Announce Type: new Abstract: Rectified-Flow (RF)-based generative models have recently emerged as strong alternatives to traditional diffusion models, demonstrating state-of-the-art performance across various tasks....
arXiv:2602.11849v1 Announce Type: new Abstract: We propose a unified framework that allows for the full mechanistic reconstruction of chemical reaction networks (CRNs) from concentration data....
arXiv:2602.11845v1 Announce Type: new Abstract: Dynamic reconstruction has achieved remarkable progress, but there remain challenges in monocular input for more practical applications. The prevailing works...
arXiv:2602.11843v1 Announce Type: new Abstract: Truncated Neumann series $S_k(A)=I+A+\cdots+A^{k-1}$ are used in approximate matrix inversion and polynomial preconditioning. In dense settings, matrix-matrix products dominate the...
arXiv:2602.11842v1 Announce Type: new Abstract: Power system security assessments, e.g. via cascading outage models, often use operational set-points based on optimal power flow (OPF) dispatch....
arXiv:2602.11841v1 Announce Type: new Abstract: Neural retrievers are effective but brittle: underspecified or ambiguous queries can misdirect ranking even when relevant documents exist. Existing approaches...
arXiv:2602.11836v1 Announce Type: new Abstract: Urdu, as a low-resource language, lacks effective semantic content recommendation systems, particularly in the domain of personalized news retrieval. Existing...