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

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arXiv:2604.08712v1 Announce Type: new Abstract: The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models and reasoning models. Recent work suggests that while LLMs have the ability to assist with domain generation, they are still far from producing high quality domains that can be deployed in practice. To this end, we investigate the ability of an agentic language model feedback framework to generate planning doma

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Computer Science > Artificial Intelligence [Submitted on 9 Apr 2026] Model Space Reasoning as Search in Feedback Space for Planning Domain Generation James Oswald, Daniel Oblinsky, Volodymyr Varha, Vasilije Dragovic, Harsha Kokel, Kavitha Srinivas, Michael Katz, Shirin Sohrabi The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models and reasoning models. Recent work suggests that while LLMs have the ability to assist with domain generation, they are still far from producing high quality domains that can be deployed in practice. To this end, we investigate the ability of an agentic language model feedback framework to generate planning domains from natural language descriptions that have been augmented with a minimal amount of symbolic information. In particular, we evaluate the quality of the generated domains under various forms of symbolic feedback, including landmarks, and output from the VAL plan validator. Using these feedback mechanisms, we experiment using heuristic search over model space to optimize domain quality. Comments: Accepted at ICLR 2026 the 2nd Workshop on World Models: Understanding, Modelling and Scaling Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.08712 [cs.AI] (or arXiv:2604.08712v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.08712 Focus to learn more Submission history From: James Oswald [view email] [v1] Thu, 9 Apr 2026 19:05:23 UTC (356 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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