arXiv:2508.00307v4 Announce Type: replace-cross Abstract: We introduce a U-net model for 360{deg} acoustic source localization formulated as a spherical semantic segmentation task. Rather than regressing discrete…
arXiv:2607.15647v1 Announce Type: new Abstract: LEED v4.1 BD+C certification remains a document-intensive process that requires reviewers to read hundreds of pages of project evidence and apply…
arXiv:2607.15829v1 Announce Type: new Abstract: Automated essay scoring (AES) enables scalable assessment and timely feedback but remains challenged by transformer input-length limitations, which can cause information…
arXiv:2607.15400v1 Announce Type: new Abstract: Falls among older adults are a major safety challenge, but continuous monitoring is difficult to sustain. Video captures fall-related posture and…
arXiv:2607.15527v1 Announce Type: new Abstract: Physical simulations that predict the behavior of urban disasters, such as climate-related flooding, play a crucial role in disaster prevention and…
arXiv:2510.19248v2 Announce Type: replace Abstract: Clustering-based features are widely used in machine learning, but most methods must choose a resolution -- a choice that is global,…
arXiv:2606.16073v2 Announce Type: replace-cross Abstract: Sampling from complex, unnormalized probability densities is a fundamental challenge in Bayesian inference and probabilistic modeling. While Markov chain Monte Carlo…
arXiv:2507.22854v3 Announce Type: replace-cross Abstract: We propose novel classical and quantum online algorithms for learning finite- and infinite-horizon Markov Decision Processes (MDPs). Our algorithms are based…
arXiv:2607.15418v1 Announce Type: new Abstract: We introduce DrawingVQA, the first benchmark designed to evaluate multimodal large language models (MLLMs) on real-world construction drawings -- a core…
arXiv:2606.16127v2 Announce Type: replace Abstract: The worldwide rise of authoritarianism and the growing role of Large Language Models (LLMs) in users' everyday lives raise the question…
arXiv:2607.15495v1 Announce Type: new Abstract: Out of everything the human brain processes, only a small fraction is consciously accessible, in the sense of being available for…
arXiv:2607.15401v1 Announce Type: new Abstract: High-quality, large-scale paired data is essential for training learning-based image deblurring models. However, synthetic blurry images generally lack realism, while real-world…
arXiv:2607.16122v1 Announce Type: cross Abstract: Evaluations should do more than measure a models current performance. They should tell us what to fix for the next model…
arXiv:2607.15449v1 Announce Type: new Abstract: Using the language of Wilsonian renormalization group theory (RG), we treat the Transformer's attention mechanism as a perturbation of the trained…
arXiv:2506.24007v5 Announce Type: replace-cross Abstract: This study investigates minimax and Bayes optimal strategies for fixed-budget best-arm identification. We consider an adaptive procedure consisting of a sampling…
arXiv:2607.16106v1 Announce Type: cross Abstract: Changepoint detection methods have seen considerable development in recent years, with online algorithms capable of identifying structural changes in streaming data…
arXiv:2607.15439v1 Announce Type: new Abstract: Our previous ARC-AGI-3 agent bundled executable world modeling, scheduled simplification, and exact replay verification, leaving unclear which idea accounted for its…
arXiv:2607.11889v2 Announce Type: replace Abstract: Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity…
arXiv:2607.15847v1 Announce Type: new Abstract: Metaphor in Arabic is a culturally grounded mechanism for constructing meaning, encoding cultural knowledge that shapes interpretation. Yet current Arabic language…
arXiv:2607.15396v1 Announce Type: new Abstract: Multi-contrast 3D MRI segmentation can be computationally demanding when all available sequences are used. We evaluate a pre-training Partial Information Decomposition…
arXiv:2607.15823v1 Announce Type: cross Abstract: We study aggregation of statistical evidence under unknown and potentially complex dependence using group-invariance. Building on permutation-based constructions that treat transformed…
arXiv:2605.15422v3 Announce Type: replace Abstract: Modern RL post-training methods such as GRPO and DAPO train on N response sequences of R tokens sampled from a shared…
arXiv:2508.01018v2 Announce Type: replace-cross Abstract: Machine learning has revitalized causal inference by combining flexible models and principled estimators, yet robust benchmarking and evaluation remain challenging with…
arXiv:2510.01915v3 Announce Type: replace-cross Abstract: We advocate for a new statistical principle that combines the most desirable aspects of both parameter inference and density estimation. This…