Hate speech in online videos is posing an increasingly serious threat to digital platforms, especially as video content becomes increasingly multimodal and context-dependent. Existing methods often struggle to effectively fuse the complex semantic relationships between modalities and lack the ability to understand nuanced hateful content. To address these issues, we propose an innovative Reasoning
Offline goal-conditioned reinforcement learning (GCRL) learns goal-reaching behaviors from static datasets, but accurate value estimation remains challenging under limited state-action coverage. Existing physics-informed approaches address this by imposing pointwise distance-like geometric constraints derived from Hamilton--Jacobi--Bellman (HJB) optimality principles, often through first-order par
Evaluating LLM agents for scientific tasks has focused on token costs while ignoring tool-use costs like simulation time and experimental resources. As a result, metrics like pass@k become impractical under realistic budget constraints. To address this gap, we introduce SimulCost, the first benchmark targeting cost-sensitive parameter tuning in physics simulations. SimulCost compares LLM tuning co
Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies. While Graph Neural Networks (GNNs) are the standard solution, state-of-the-art pipelines often rely on explicit structural heuristics or memory-intensive node embeddings -- approaches that struggle to generalize or scale to massive graphs. Emerging Graph Transform
Hyperspectral image (HSI) classification typically involves large-scale data and computationally intensive training, which limits the practical deployment of deep learning models in real-world remote sensing tasks. This study introduces SpectralTrain, a universal, architecture-agnostic training framework that enhances learning efficiency by integrating curriculum learning (CL) with principal compo
Named moves are too sparse to show a hiring pattern, but the desk still flags formation and join events that can matter when they cluster around a team rebuild.
Named moves are too sparse to show a hiring pattern, but the desk still flags formation and join events that can matter when they cluster around a team rebuild.
Named moves are too sparse to show a hiring pattern, but the desk still flags formation and join events that can matter when they cluster around a team rebuild.
Three fresh scores on one model tighten attention on a familiar evaluation surface, with reasoning, math, and general knowledge all updated together.
Three fresh scores on one model tighten attention on a familiar evaluation surface, with reasoning, math, and general knowledge all updated together.
Three fresh scores on one model tighten attention on a familiar evaluation surface, with reasoning, math, and general knowledge all updated together.