neural networks

mechanistic interpretability

MINAR: Opening the Black Box of Neural Algorithmic Reasoning

New research introduces MINAR framework for understanding how neural networks learn to execute algorithms, advancing interpretability methods critical for AI safety and verification.

neural networks

SymTorch: Extracting Symbolic Math from Neural Networks

New framework converts opaque neural network decisions into interpretable mathematical expressions, enabling better model verification and understanding of AI behavior.

world models

World Models Explained: The AI Architecture Reshaping Video

World models enable AI to simulate reality by learning internal representations of environments. This foundational architecture powers next-gen video generation, robotics, and autonomous systems.

LLM

LLM Parameters Explained: Weights, Biases, and Scale Demystified

Understanding LLM parameters is key to grasping how AI models generate text, images, and video. Learn what weights and biases actually do and why model scale matters.

LLM Training

2:4 Sparsity Breakthrough: Neuron-Level Activation for Faster LLM

New research introduces neuron-level activation functions that leverage 2:4 structured sparsity to dramatically accelerate LLM pre-training while maintaining model quality.

Edge AI

HQP: Hybrid Quantization-Pruning for Edge AI Inference

New research combines sensitivity-aware quantization and pruning to enable ultra-low-latency AI inference on edge devices, potentially transforming how generative models deploy on mobile hardware.

LLM research

New Method Internalizes LLM Reasoning Through Latent Actions

Researchers propose a novel approach to improve LLM reasoning by discovering and replaying latent actions, potentially reducing inference costs while maintaining reasoning quality.

LLM research

Activation Steering: How Reasoning-Critical Neurons Improve LLM R

New research identifies specific neurons responsible for reasoning in LLMs and demonstrates how transferring their activation patterns can significantly improve inference reliability across models.

neural networks

Differential Geometry Unlocks Neural Network Information Flow

New research applies differential geometry to analyze how information propagates through neural networks, offering mathematical tools to understand deep learning architectures at a fundamental level.

AI Research

AI Memory Systems: How Close Are We to Human Hippocampus?

New research examines the gap between AI memory architectures and the human hippocampus, exploring how neuroscience insights could transform machine learning systems.

Deep Learning

NOVAK: A Unified Adaptive Optimizer for Deep Neural Networks

New research introduces NOVAK, a unified framework that bridges popular adaptive optimizers like Adam and AdaGrad, potentially improving training efficiency for deep learning models.

Explainable AI

xDNN(ASP): Logic-Based Explainability for Neural Networks

New research combines deep neural networks with Answer Set Programming to generate human-readable explanations for AI decisions, advancing interpretability crucial for detection systems.