Neural Networks

A developer built a Transformer-based time-series forecaster using PyTorch to predict blood glucose levels 30 minutes in advance from Continuous Glucose Monitoring (CGM) data. The model, which uses self-attention mechani…

Tesla has built an AI system that combines supervised, unsupervised, and reinforcement learning into a single architecture that improves every time a customer drives, according to an analysis of the company's strategy. T…

Astrum Verum, an open-source research project, has released a vector symbolic memory architecture called CognitiveMemory that outperforms standard Retrieval-Augmented Generation (RAG) on structured fact recall. The syste…

OpenAI trained a reward model using a loss function that does not require defining ideal reward values in advance. The loss function, introduced in OpenAI's 2022 paper, guides the model to assign higher rewards to prefer…

Neuroscientist Christof Koch argued at an April 2026 symposium in Porto that consciousness is a fundamental feature of reality, similar to gravity or electric charge, rather than a product of brain activity. Koch's propo…

The University of Toronto's Computational Social Science Lab released and open-sourced Maia-3, a chess engine that predicts human moves rather than optimal ones, on Tuesday. The model achieves a 57.1% prediction accuracy…

NVIDIA's InfiniBand became the dominant networking fabric for AI datacenters because it was purpose-built for lossless, high-throughput GPU-to-GPU communication. The shift to AI training fundamentally changed datacenter …

Yann LeCun, a 2018 Turing Award winner and pioneer of deep learning, discussed the rapid adoption of neural networks in speech recognition, image recognition, and natural language processing over the past fifteen years. …

Researchers have developed PiD, a pixel diffusion decoder that directly transforms latent representations into high-resolution images, bypassing the traditional decode-then-super-resolve pipeline. The system decodes 512×…

MathWorks hosted a webinar demonstrating an end-to-end workflow for designing, training, and deploying AI-based virtual sensor models to embedded processors within a single environment. The session highlighted techniques…

Researchers introduced ImProver 2, a neurosymbolic framework for automated proof optimization in Lean 4 that combines a data-efficient expert-iteration pipeline with a scaffold exposing formal structure and lightweight i…

A new method for measuring uncertainty in language model outputs, developed by researchers and detailed in a preprint on arXiv, extracts geometric features from the model's internal layer-by-layer processing to outperfor…

Researchers have developed WeCon, a weight-conditioned neural solver that achieves comparable solution quality to state-of-the-art methods while reducing inference time by 40% across multiple multi-objective combinatoria…

Researchers introduced Tensor Cache, a two-level memory system for Transformer models that stores evicted key-value pairs from a sliding-window cache into a compressed outer-product fast-weight memory. The approach uses …

New research shows that exact certification of neural networks—determining the smallest set of labeled examples needed to guarantee a learned hypothesis matches the target—becomes exponentially hard under minimal overpar…

Researchers have found that chain-of-thought reasoning in large language models is only beneficial when early-stage entropy dynamics show consistent reduction, according to a new study on arXiv. The team introduced EDRM,…

Researchers have introduced Human-Centered Learning Mechanics (HCLM), a dynamical and information-theoretic framework that treats deep learning as an open system under uncertainty, resource constraints, and human feedbac…

A team of researchers improved a DETR-based fusion transformer for the MaCVi 2026 Vision-to-Chart data association challenge by adding a dedicated MLP, QueryMLP, to explicitly predict a buoy's waterline contact point in …

Researchers demonstrated that multi-level Floyd-Steinberg error-diffusion dithering, a lightweight input transformation, can disrupt adversarial attacks against vision foundation models while preserving semantic content.…

Flow Mismatching, a new unsupervised anomaly detection method, avoids reconstruction-based paradigms by treating flow matching as geometric dynamics and identifying anomalies where learned normal flow disagrees with the …