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

Researchers present a novel approach using agentic language model feedback frameworks to generate planning domains from natural language descriptions augmented with symbolic information. The method employs heuristic search over model space optimized by various feedback mechanisms, including landmarks and plan validator outputs, to improve domain quality for practical deployment.

This research addresses a critical limitation in artificial intelligence: the difficulty of translating natural language into executable planning domains, even as large language models have become more sophisticated. While LLMs demonstrate capability in assisting with domain generation, the quality gap between generated and manually-crafted domains remains substantial, preventing widespread adoption in production environments. The research investigates whether structured feedback mechanisms can bridge this gap through iterative refinement.

The approach combines symbolic reasoning with language model capabilities by augmenting natural language descriptions with minimal symbolic information. Rather than attempting single-pass generation, the researchers employ heuristic search across model space—essentially trying multiple variations and scoring them against feedback signals. This methodology draws from planning theory and applies it to the meta-problem of domain generation itself, treating model improvement as a search problem with defined evaluation metrics.

The technical contribution lies in demonstrating that feedback from plan validators and landmark detection—classical planning analysis tools—can effectively guide language models toward better domain specifications. This bridges two traditionally separate fields: classical planning and modern language models. For AI practitioners, this suggests that hybrid approaches combining symbolic constraints with neural capabilities may prove more effective than pure neural approaches for structured generation tasks.

Looking forward, similar feedback-driven optimization patterns may improve other structured generation problems where quality metrics are well-defined but difficult to directly optimize. The work indicates that agentic frameworks with external feedback loops represent a promising direction for moving AI systems from demonstration to practical deployment in specialized domains.

• →LLMs struggle to generate high-quality planning domains suitable for practical deployment despite their general capability.
• →Augmenting natural language with minimal symbolic information enables more effective domain generation through structured feedback.
• →Heuristic search over model space using validator feedback and landmarks optimizes domain quality iteratively.
• →Hybrid approaches combining symbolic planning tools with language models outperform single-pass neural generation.
• →Agentic feedback frameworks represent a viable path toward production-ready AI systems for specialized tasks.