Building
intelligent
systems.
Researching the architectures, components and infrastructure required to build increasingly capable machine intelligence.
Intelligence
needs architecture.
A powerful model is only one component of an intelligent system. Real-world intelligence requires mechanisms for context, memory, reasoning, planning, tool use, interaction and evaluation.
KryomAI studies how these components can be combined into coherent architectures capable of operating across increasingly complex environments.
From models
to intelligent systems.
We explore the layers surrounding core models that allow machine intelligence to become useful, reliable and increasingly autonomous.
Foundation Models
Core models that provide perception, language understanding, generation and general-purpose capabilities.
Context & Memory
Systems for maintaining context, retrieving knowledge and creating persistent machine memory.
Reasoning
Architectures that enable structured inference, problem solving, planning and decision making.
Tool Use
Connecting intelligence to external tools, APIs, software environments and information sources.
Agents
Systems that transform intelligence into multi-step processes capable of pursuing objectives.
Evaluation
Measuring capability, reliability, robustness and the ability of intelligent systems to operate effectively.
Core capabilities
of intelligence.
Perception
Processing language, vision, audio and multimodal information.
Knowledge
Representing, retrieving and applying information across contexts.
Reasoning
Analyzing problems, evaluating possibilities and constructing solutions.
Planning
Transforming objectives into structured sequences of actions.
Adaptation
Learning from interaction, feedback and changing environments.
Action
Using tools and environments to turn decisions into outcomes.
Intelligence
in motion.
Intelligent systems continuously transform information into decisions, actions and new information.
Perceive
Receive information from the environment.
Reason
Analyze information and evaluate possibilities.
Decide
Select strategies and determine next actions.
Act
Execute actions and observe their outcomes.
Toward more
general systems.
As intelligent systems become more capable, the boundary between models, agents, tools and environments becomes increasingly important.
KryomAI's long-term direction is to investigate architectures that can integrate these capabilities into increasingly general forms of machine intelligence.
Models
Learn and represent information.
Systems
Combine capabilities into coherent architectures.
Agents
Operate through objectives and environments.
Intelligence
Generalize across tasks and domains.