arXiv:2511.00421v1 Announce Type: cross Abstract: Large language models (LLMs) show increasing promise in medical applications, but their ability to detect and correct errors in clinical texts — a prerequisite for safe deployment — remains under-evaluated, particularly beyond English. We introduce MedRECT, a cross-lingual benchmark (Japanese/English) that formulates medical error handling as three subtasks: error detection, […]
Interaction as Intelligence Part II: Asynchronous Human-Agent Rollout for Long-Horizon Task Training
arXiv:2510.27630v2 Announce Type: replace Abstract: Large Language Model (LLM) agents have recently shown strong potential in domains such as automated coding, deep research, and graphical user interface manipulation. However, training them to succeed on long-horizon, domain-specialized tasks remains challenging. Current methods primarily fall into two categories. The first relies on dense human annotations through behavior […]
Why Federated Optimization Fails to Achieve Perfect Fitting? A Theoretical Perspective on Client-Side Optima
arXiv:2511.00469v1 Announce Type: cross Abstract: Federated optimization is a constrained form of distributed optimization that enables training a global model without directly sharing client data. Although existing algorithms can guarantee convergence in theory and often achieve stable training in practice, the reasons behind performance degradation under data heterogeneity remain unclear. To address this gap, the […]
Advancing AI Challenges for the United States Department of the Air Force
arXiv:2511.00267v1 Announce Type: new Abstract: The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent […]
Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
arXiv:2511.00549v1 Announce Type: cross Abstract: Traffic congestion, primarily driven by intersection queuing, significantly impacts urban living standards, safety, environmental quality, and economic efficiency. While Traffic Signal Control (TSC) systems hold potential for congestion mitigation, traditional optimization models often fail to capture real-world traffic complexity and dynamics. This study introduces a novel single-agent reinforcement learning (RL) […]
A Low-Resolution Image is Worth 1×1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift
arXiv:2411.10231v2 Announce Type: replace-cross Abstract: Transformer-based architectures have recently advanced the image reconstruction quality of super-resolution (SR) models. Yet, their scalability remains limited by quadratic attention costs and coarse patch embeddings that weaken pixel-level fidelity. We propose TaylorIR, a plug-and-play framework that enforces 1×1 patch embeddings for true pixel-wise reasoning and replaces conventional self-attention with […]
EPARA: Parallelizing Categorized AI Inference in Edge Clouds
arXiv:2511.00603v1 Announce Type: cross Abstract: With the increasing adoption of AI applications such as large language models and computer vision AI, the computational demands on AI inference systems are continuously rising, making the enhancement of task processing capacity using existing hardware a primary objective in edge clouds. We propose EPARA, an end-to-end AI parallel inference […]
SCUDDO: An unsupervised clustering algorithm for single-cell Hi-C maps using diagonal diffusion operators
arXiv:2511.00278v1 Announce Type: new Abstract: Motivation: Advances in high-throughput chromatin conformation capture have provided insight into the three- dimensional structure and organization of chromatin. While bulk Hi-C experiments capture spatio-temporally averaged chromatin interactions across millions of cells, single-cell Hi-C experiments report on the chromatin interactions of individual cells. Supervised and unsupervised algorithms have been developed […]
Metadata-Aligned 3D MRI Representations for Contrast Understanding and Quality Control
arXiv:2511.00681v1 Announce Type: cross Abstract: Magnetic Resonance Imaging suffers from substantial data heterogeneity and the absence of standardized contrast labels across scanners, protocols, and institutions, which severely limits large-scale automated analysis. A unified representation of MRI contrast would enable a wide range of downstream utilities, from automatic sequence recognition to harmonization and quality control, without […]
Vision-Language Model-Based Semantic-Guided Imaging Biomarker for Lung Nodule Malignancy Prediction
arXiv:2504.21344v3 Announce Type: replace-cross Abstract: Machine learning models have utilized semantic features, deep features, or both to assess lung nodule malignancy. However, their reliance on manual annotation during inference, limited interpretability, and sensitivity to imaging variations hinder their application in real-world clinical settings. Thus, this research aims to integrate semantic features derived from radiologists’ assessments […]