Can intelligent systems examine their own reasoning?
Metacognition is the ability of a system to monitor its own decisions, detect possible errors, assess uncertainty, and adapt its strategy when needed.
Most AI systems are built to generate actions or responses. They rarely examine the reasoning process that produced those outputs.
As a result, agents may continue following flawed reasoning, overlook uncertainty, or fail to recognize when a strategy should change.
Metacognity studies how AI agents can evaluate their own reasoning and respond when that reasoning begins to fail.
The work focuses on mechanisms that allow agents to detect uncertainty, recognize flawed reasoning, and adapt their strategy when the current approach is no longer working.
AI agents are becoming increasingly capable of planning and acting in complex environments. Yet most systems still lack mechanisms for evaluating the quality of their own reasoning.
Developing practical forms of metacognitive control could significantly improve the reliability and adaptability of AI systems.
This project is in an early research phase. More material, experiments, and publications will appear here as the work develops.
For collaboration or inquiries:
contact@metacognity.ai