Neural Networks

Researchers have developed the Iterative Refinement Neural Operator (IRNO), a new AI architecture that improves scientific modeling by iteratively correcting errors through fixed-point iteration, directly addressing the …

Researchers introduced BoxLitE, a knowledge base embedding model that uses convex optimization to faithfully represent both factual data and conceptual hierarchies in DL-Lite$^{\mathcal{H}}$ ontologies. The model maps co…

Researchers developed GazeWorld, a medical imaging model that learns from radiologists' eye-tracking data by treating their fixation sequences as trajectories through an image. The model autoregressively predicts latent …

A systematic review of 75 studies published between 2017 and 2025 found that the effectiveness of self-supervised learning in medical imaging depends on aligning the pretext task with the downstream clinical objective, w…

Researchers have developed Diff-Instruct with Diffused Reward (DIDR), a data-free trajectory-level alignment framework that propagates reward-tilted clean-image distributions across all noise levels to improve one-step t…

Researchers compared deep learning models against linear interpolation for reconstructing missing spectral bands in PlanetScope SuperDove satellite imagery across four lakes with algal bloom histories. The study found th…

Researchers introduced ActQuant, a sub-4-bit quantization framework for Vision-Language-Action (VLA) models that reduces computational demands for edge deployment. The method achieved 95% performance retention at 3 bits-…

Researchers developed fMRI-Diffusion, a framework that generates synthetic fMRI time series using a Temporal Transformer diffusion model to improve Major Depressive Disorder diagnosis. The method outperformed existing au…

Researchers have developed EMMA, a physics-informed multimodal framework that recovers all identifiable dynamical parameters of a system directly from raw video, audio, and image-based time-series observations. The frame…

Researchers have developed a brain-to-image system that decodes visual stimuli from EEG signals, achieving a mean Top-1 retrieval accuracy of 86.30% and a CLIP score of 0.903 for image reconstruction. The system uses mul…

Researchers have introduced KREPE, the first generative representation learning method for hyper-relational knowledge graphs (HKGs) that can generate valid facts from arbitrarily masked queries, including completing part…

Researchers have developed a bifurcation theory that detects when neural networks acquire structured representations during training in real time, without relying on retrospective or label-dependent metrics. The theory i…

Researchers replicated the open-ended image evolution platform Picbreeder by replacing human users with frontier Vision-Language Models (VLMs), finding clear qualitative differences between AI-generated and human-generat…

Researchers found that removing 25% of transitions from reinforcement learning rollout data stabilizes PPO training by breaking repetitive gradient structures caused by causally chained states. The method, requiring only…

Researchers introduced Verifiable Transformers, a framework that converts localized circuits inside Transformer models into bounded, solver-checkable claims using SMT solvers. The approach directly verifies properties li…

Researchers have developed Concept-Aware Fault Detection (CAFD), a learning-based method that improves fault detection in deep neural networks by integrating model-based, distance-based, and a novel concept-based feature…

Researchers introduced Verified SHAP, an algorithm that uses neural network verification to compute exact lower and upper bounds on Shapley values for neural networks. The method scales to search spaces orders of magnitu…

A new Uncertainty-Aware Physics-Informed Neural Network (PINN) framework overcomes "Physics Shock" — catastrophic gradient divergence caused by enforcing rigid flood equations onto noisy satellite radar data — by dynamic…

Researchers developed Riemannian Archetypal Analysis, a method that combines the interpretability of classical archetypal analysis with the flexibility of non-linear models by using data-driven pullback geometry on defor…

A developer built a new programming language, Jda, that compiles 33 times faster than Rust, achieving an average compile time of 42 milliseconds. The single-pass compiler produces 1MB static binaries and includes built-i…