Researchers introduced Sentinel Challenge, a benchmark testing how decentralized embodied agents coordinate in city-scale outdoor environments by communicating in natural language to agree on safe meeting points while av…
Researchers have released RoMo, a large-scale dataset of in-the-wild human motions curated to overcome the limitations of existing small, high-fidelity motion capture and low-quality collections. The dataset employs a ta…
Researchers have developed a new emotion representation method called Dimensional Distribution Emotion State (DDES) to help museum curators predict the emotional responses artworks will evoke in visitors. The approach us…
Researchers have developed VesselSim, a two-stage framework for 3D blood vessel segmentation that eliminates the need for expert annotations during training by generating 16,500 synthetic angiographic volumes. The system…
A unified evaluation of twelve deep learning models for binary skin cancer detection on the PAD-UFES-20 dataset found that well-tuned CNNs provide strong baselines, but transformer-based families consistently improve dis…
Researchers have introduced GEM (Geometric Entropy Mixing), a framework that reformulates LLM data curation as a variational problem on a hypersphere to overcome flaws in human taxonomies and Euclidean clustering. By dec…
A new study from researchers measuring validity-correctness tradeoffs in structured outputs for small language models found that hard schema constraints significantly degrade answer accuracy despite perfect schema compli…
AirCast-SR, a new foundation model for atmospheric super-resolution, downscales global AI weather forecasts from 28-kilometer to 1-kilometer horizontal resolution at hourly intervals, producing 67-hour forecasts of eight…
Researchers have introduced Neural Bayesian Sequential Routing (NBSR), a framework that models neural inference as active evidence accumulation over a hierarchical Directed Acyclic Graph using a Dirichlet-Categorical con…
Researchers have developed a neural rule evaluator that detects anomalies by identifying violations of logical constraints, even when such violations are absent from training data. The system compiles rules into directed…
A new study reveals that language model reasoning trajectories during test-time sampling cluster into "reasoning basins," causing majority votes to favor stable but potentially incorrect answers. The researchers introduc…
Researchers have developed InfoQuant, a training-free method that reshapes activation distributions in large language models to improve low-bit quantization efficiency. The approach, which uses Peak Suppression Orthogona…
Researchers have developed GAC, a noise-aware adaptive mixing controller for hybrid post-training that combines supervised fine-tuning and reinforcement learning. The method dynamically adjusts the mixing weight between …
Researchers at OpenPangu identified two failure modes in HiF8 W8A8 quantization-aware training for the OpenPangu-Embedded-1B model: amax saturation, which corrupts knowledge-sensitive representations through forward-pass…
Researchers developed HRVConformer, a deep learning architecture that classifies neonatal hypoxic-ischemic encephalopathy (HIE) by directly processing raw heart rate signals, achieving an AUC of 83.23% and accuracy of 74…
Researchers have developed AIMS-Fold, a guided-diffusion framework that integrates structural proteomics data from XL-MS and HDX-MS to improve protein complex structure prediction. The model outperforms purely computatio…
Researchers have developed CoAD, a new framework that unifies classification and reconstruction approaches to improve time series anomaly detection. The framework uses a classification module to generate probability-info…
Researchers at arXiv introduced PathoFM, an encoder-centric transformer for clinical time-series learning that uses three complementary pretraining objectives—Local Completion, Temporal Continuity, and Unsupervised In-Co…
Researchers have proven a finite-sample bound on the approximate max-information of differentially private stochastic gradient descent (DP-SGD) that scales at most linearly with dataset size, matching classic results for…
A new research paper from arXiv proposes Governed Evolving Memory (GEM), a data-management framework that redefines long-term AI agent memory as a state trajectory rather than record storage. The authors argue that curre…