Modulating Cross-Modal Convergence with Single-Stimulus, Intra-Modal Dispersion
Study shows neural network representational convergence across architectures and modalities using Procrustes analysis, linking to brain alignment.
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Study shows neural network representational convergence across architectures and modalities using Procrustes analysis, linking to brain alignment.
Reasoning LLMs often spend substantial tokens on long intermediate reasoning traces (e.g., chain-of-thought) when solving new problems. We propose to summarize and store reusable reasoning skills distilled from extensive deliberation and trial-and-error exploration, and to…
Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physical phenomena. By incorporating physical laws into the learning process, PINN models have…
CRNN surrogate models for crystal growth dynamics simulation with explicit and implicit supersaturation conditioning architectures.
N-gram models match LSTM/Transformer accuracy on event-log prediction with lower resources and better stability than neural baselines.
Higher-order optimization algorithms such as Shampoo have been effectively applied in neural network training for at least a decade. These methods have achieved... Higher-order optimization algorithms such as Shampoo have been effectively applied in neural network training for…
Ace is the first robot that can beat the best human players while following the official rules of table tennis. | Image: Sony AI Humans have been building ping-pong playing robots for decades, such as Omron's FOREPHUS that challenged amateur competitors at CES 2017. What sets…
Gauge-equivariant graph neural networks embed non-Abelian symmetries into message passing for lattice gauge theory problems.
AAC: differentiable landmark compressor for ALT shortest-path heuristics with admissibility-by-construction.
Simulation-Based Inference with neural posterior estimation for industrial equipment degradation monitoring; domain-specific application without novel AI methods.
Likelihood factorization for hierarchical SBI reduces simulator costs by training neural surrogates per-site instead of multi-site sampling.
QuanForge mutation testing framework for Quantum Neural Networks addressing stochastic factors and quantum measurement randomness.
StormNet applies graph neural networks to bias-correct storm surge forecasts from ADCIRC models.
Feature whitening improves interpretability of linear neuroimaging models for brain biomarker discovery.
Study links calibration, curvature, and margins during neural network training; Expected Calibration Error tightly coupled to Hessian properties.
Meta employees' activity at work is now being used to train the company's AI agents. As reported by Reuters, Meta is installing a tool it calls Model Capability Initiative (MCI) on US-based employees' computers that runs in work-related apps and websites, recording mouse…
SmartVector adds temporal, confidence, and relational metadata to embeddings to improve RAG accuracy on versioned queries from 58% baseline.
S4D state space models mapped to nonlinear oscillator networks, enabling interpretability of long-range dependency capture via wave-based encoding.
Vine Denoising Copula amortizes vine-copula modeling via reusable bivariate denoising for tractable high-dimensional density estimation.
10x Science has raised a $4.8 million seed round to help pharmaceutical researchers understand complex molecules.
Graph neural network predicts network-level traffic flows by modeling heterogeneous bidirectional graphs of IP, Port, and Connection nodes.
Static duplicate detection tool for Cucumber BDD test steps using embeddings and hashing across public repositories.
Anthropic's Mythos AI model, a powerful cybersecurity tool that the company said could be dangerous in the wrong hands, has been accessed by a "small group of unauthorized users," Bloomberg reports. An unnamed member of the group, identified only as "a third-party contractor…
NeurIPS 2026 thread: researchers debate whether to submit code alongside papers given trade-offs between credibility and plagiarism risk.
Neural networks trained at edge of stability converge to fractal attractors; Lyapunov dimension theory explains improved generalization from chaotic optimization.
Central limit theorems for wide random neural networks on spheres show three distinct regimes based on covariance function fixed points.
HardNet++ enforces nonlinear constraint satisfaction in neural network outputs for safety-critical control and decision-making applications.
Neural operator framework discovers stability and receptivity in complex systems from data alone, without governing equations.
TACENR provides task-agnostic contrastive explanations for node representations in graph neural networks.
Conditioning consistency gap quantifies KL divergence between Conditional Neural Process predictions when context points are added.