Model Space Reasoning as Search in Feedback Space for Planning Domain Generation

This research investigates using an agentic language model feedback framework to generate high-quality planning domains from augmented natural language descriptions. It evaluates the impact of various symbolic feedback mechanisms, like landmarks and plan validation output, in conjunction with heuristic search over model space to optimize domain quality.