Machine learning news — deep learning, reinforcement learning, neural architecture search, diffusion models, and new ML frameworks and libraries.
Researchers submitted a system to the SoccerNet 2026 Player-Centric Ball Action Spotting challenge that uses a two-stage pipeline with per-player attention and agreement-based ensembling, improving Macro-F1 from 48.6 to …
Researchers propose Hierarchical Temporal Fusion (HTF), an extension of the Temporal Fusion Transformer that integrates hierarchical embeddings and a coherence-aware loss function to enforce forecast consistency across h…
Researchers propose a memory-augmented LSTM autoencoder for unsupervised human activity recognition using IMU sensor fusion, achieving 96.6% and 98.4% accuracy on DaLiAc and PAMAP2 datasets, outperforming supervised base…
Researchers propose an agentic AI pipeline for appliance-level energy anomaly detection in office buildings, combining deep time-series forecasting, variational anomaly detection, and LLM-based reasoning to generate prio…
Researchers propose a semantic-aware generative image transmission framework for resource-constrained visual IoT systems. The method selects and transmits only task-relevant image tokens based on semantic importance, ach…
Researchers developed an incremental approximate message passing (IAMP) algorithm for empirical risk minimization under a multi-index model, achieving near-optimal training and test error in high-dimensional asymptotics.…
Researchers introduced the Covariance-Agnostic Neural Kalman Consensus Filter (CA-NKCF), a distributed sensing framework that combines partial domain knowledge with deep neural networks for latent state estimation withou…
Reinforcement learning researchers need to clearly distinguish between using simulators as a proxy for real-world deployment and solving simulators as an end in itself, according to a new paper. The authors argue that co…
Researchers introduced SciDraw-Bench, a benchmark for evaluating scientific figure generation by text-to-image and multimodal models, covering 32 tasks across eight figure types and ten disciplines. In a pilot study, a d…
Researchers at arXiv found that using large language models (LLMs) like GPT-5.2 to label training data for entity matching can reduce manual labeling effort by hundreds of hours, with student models performing within two…
Researchers at arXiv propose that meta-learning, or learning to learn, may explain why human visual representations are more flexible than those of pretrained neural networks. By training a sequence model on thousands of…
Researchers introduced Jenga Inverse Predictor (JIP-2), a GPU-accelerated deep learning framework that reconstructs collapsed stone structures by treating anastylosis as an inverse prediction task. The system uses a phys…
Researchers introduced ATHENA-R1, an AI agent trained via reinforcement learning over 212 biomedical tools to perform treatment reasoning across all FDA-approved drugs since 1939. The agent achieved 94.7% accuracy on dru…
Researchers propose DCSNet, an end-to-end framework for small object segmentation in medical imaging that integrates Detection-guided Hierarchical Cropping and Multiscale Feature Aggregation to overcome class imbalance a…
Researchers introduced JASPR, a self-supervised deep learning framework that integrates histology images and spatial transcriptomics data to improve virtual genomic screening and clinical prognostication. Trained on brea…
Researchers propose RSGPNet, a training-free geometric prompting framework for open-vocabulary semantic segmentation in remote sensing, which improves segmentation accuracy by leveraging object geometric areas and consis…
Researchers proposed a scalable operator-learning framework, KL-DNN, for large-scale PDE problems, achieving 1.1 psi RMSE for pressure and 0.0146 for CO2 saturation in geological carbon storage simulations. The model tra…
Researchers at arXiv show that static per-layer Fibonacci spacing in sparse attention outperforms learned dilation and fixed schedules across 21 language models, achieving better perplexity and extrapolating to four time…
Researchers introduced RSEA, a Recursive Self-Evolving Agent that improves LLM agents by rewriting natural-language artifacts without weight updates, using a held-out selection gate to prevent regression. Across four ben…
Researchers propose AnTenA, a system that uses large language models to explain hidden patterns in multi-aspect data without relying on potentially inaccurate labels or metadata. The system employs tensor decomposition a…