agi models
Token-Based Language Models
Examples: GPT-4.5, DeepSeek, Claude 3.5 Sonnet, Phi-4, Grok 3.
These large language models exhibit multimodal fluency and partial reasoning but are limited by token-level prediction.
Modular Reasoning Systems
Architectures composed of specialized, interacting components that support flexible, compositional reasoning for complex tasks.
Persistent Memory Architectures
Memory systems that maintain context and knowledge across tasks and time, enabling learning, adaptation, and coherent behavior.
Multi-Agent Coordination Frameworks
Systems that allow multiple intelligent agents to collaborate and coordinate toward shared goals, enhancing problem-solving capabilities.
See: ai agents
Agentic RAG (Retrieval-Augmented Generation) Frameworks
Architectures that dynamically retrieve information, plan, and use tools in real-time to support adaptive and goal-directed behavior.
Information Compression and Generalization Strategies
Includes test-time adaptation and training-free methods that enable models to generalize flexibly across domains without extensive retraining.
Vision-Language Models (VLMs)
Multimodal systems that integrate visual and textual data, functioning as perception interfaces for embodied understanding and collaboration.
Neurosymbolic Systems
Hybrid models that combine neural networks with symbolic logic, enabling structured reasoning alongside statistical learning.
Reinforcement Learning (RL)
Training approach where agents learn optimal behaviors through interactions and rewards in goal-driven environments.
Cognitive Scaffolding Techniques
Methods that structure and guide learning and reasoning, inspired by cognitive science, to support self-improvement and task mastery.