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. - 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.