AI Agents
AI Agent Architectures: A Complete Technical Guide
From single-agent loops to multi-agent orchestration, a comprehensive overview of every major AI agent architecture pattern driving autonomous systems today.
Architecture
neural-networks
SPM Architecture Achieves Near-Linear Training for Neural Network
New Stagewise Pairwise Mixing method replaces dense linear layers with O(n log n) complexity, potentially revolutionizing how large AI models are trained.
AI Agents
Shallow vs Deep AI Agents: Architectural Fundamentals
Understanding the critical architectural differences between shallow and deep AI agents—from simple reactive systems to complex multi-layered reasoning frameworks that enable autonomous decision-making and adaptive behavior.
machine learning
Self-Evolving AI: Dynamic Nested Hierarchies Framework
New research introduces Dynamic Nested Hierarchies (DNH), an architecture enabling ML systems to autonomously evolve their structure during training. Framework addresses catastrophic forgetting in lifelong learning through self-organizing hierarchical components.
AI Agents
Neuro-Symbolic-Causal Architecture for Robust AI Agents
New research proposes integrating neural networks, symbolic reasoning, and causal inference into a unified architecture for AI agents that can handle complex multi-objective tasks with improved robustness beyond traditional prompt engineering.