neural-networks

Sphere Neural Networks: Guaranteed Decision Regions for AI

New neural architecture creates mathematically guaranteed decision regions using hyperspheres, enabling AI systems to know when they're uncertain rather than making unreliable predictions.

interpretable AI

Decoding the Black Box: Comparing Interpretable ML Methods

A comprehensive study compares leading interpretable ML techniques including SHAP, LIME, and attention mechanisms, providing crucial insights for building transparent AI systems in detection and authenticity applications.

LLM infrastructure

FlashInfer-Bench: New Framework Optimizes LLM Kernel Performance

Researchers introduce FlashInfer-Bench, a comprehensive benchmarking suite that creates a virtuous cycle for optimizing attention kernels in LLM serving systems, addressing critical infrastructure needs.

AI detection

Unknown-Aware Attribution: Identifying AI Content Origins

New research tackles the challenge of attributing AI-generated content to specific models while handling unknown generators—critical for deepfake detection and digital authenticity verification.

LLM Security

Vocabulary Trojans: A New Threat to LLM Security and Trust

Researchers reveal how malicious actors can embed hidden backdoors in LLMs through vocabulary manipulation, enabling stealthy sabotage that evades detection methods.

Meta AI

V-JEPA 2 vs Sora: Why Pixel Generation Isn't Planning

Meta's V-JEPA 2 challenges the assumption that generating photorealistic video means understanding the world. The architecture reveals why predicting latent representations may outperform pixel-level synthesis.

Diffusion Models

How Fourier's 200-Year-Old Heat Equation Powers AI Image Generati

The mathematics behind AI image generators like Stable Diffusion traces back to Joseph Fourier's 1822 heat equation. Understanding diffusion processes reveals how these models transform noise into coherent images.

deepfake detection

Military Recruits ROTC Students for Deepfake Defense Program

The U.S. military is enlisting ROTC students in the fight against AI-generated disinformation, training the next generation of defenders against synthetic media threats.

LLM

LLM Quantization: Cut Model Size 75% Without Losing Accuracy

Quantization and fine-tuning techniques like QLoRA can reduce large language model sizes by 75% while preserving performance, enabling efficient AI deployment on consumer hardware.

machine learning

Gradient Descent Explained: The Core Algorithm Behind AI

Understanding gradient descent is essential to grasping how neural networks learn. This foundational optimization algorithm powers everything from deepfake generators to detection systems.

LLM

HaluNet: Multi-Granular Uncertainty for LLM Hallucination Detecti

New research introduces HaluNet, a framework using multi-granular uncertainty modeling to efficiently detect hallucinations in LLM question answering systems.

LLM

Entropy-Aware Speculative Decoding Boosts LLM Reasoning

New research introduces entropy-based adaptive speculation that detects reasoning phases in LLMs, dynamically adjusting decoding strategies to improve both speed and output quality.