2026
A Metacognitive Architecture for Correcting LLM Errors in AI Agents
AAAI 2026technical
The ability to correct mistakes and adapt to users
3 accepted papers
The ability to correct mistakes and adapt to users
Modular AI systems can be developed using LLM-prompts-based modules to minimize deployment time even for complex tasks. However, these systems do not always perform well and improving them using the data traces collected from a deployment remains an open challenge. The data traces contain LLM inputs…
When a robot adapts a learned task for a novel environment, any changes to objects in the novel environment have an unknown effect on its task execution. For example, replacing an object in a pick-and-place task affects where the robot should target its actions, but does not necessarily affect the u…