Zero-Shot Goal Recognition with Large Language Models

Computer Science > Artificial Intelligence [Submitted on 14 May 2026] Zero-Shot Goal Recognition with Large Language Models Kin Max Piamolini Gusmão, Nathan Gavenski, Nir Oren, Felipe Meneguzzi Large language models have recently reached near-parity with classical planners on well-known planning domains, yet this competence relies on world-knowledge exploitation rather than genuine symbolic reasoning. Goal recognition is a complementary abductive task structurally better suited to LLM strengths: it consists of evaluating consistency with world knowledge rather than generating novel action sequences. This paper provides the first systematic zero-shot evaluation of frontier LLMs as goal recognisers on key classical PDDL benchmarks. Our results show that LLM competence on goal recognition is uneven: some models scale with evidence and approach landmark-based accuracy at full observations, while others remain anchored to world-knowledge priors regardless of how much evidence accumulates. Qualitative analysis of model reasoning traces reveals that this divergence reflects a fundamental difference in evidence integration rather than domain familiarity. These findings position goal recognition as a principled benchmark for the foundational planning knowledge of LLMs. Comments: 9 pages, 1 figure, 1 table; appendix with 8 figures and 2 code listings (29 pages total); submitted to NeurIPS 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.15333 [cs.AI]   (or arXiv:2605.15333v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.15333 Focus to learn more Submission history From: Felipe Meneguzzi [view email] [v1] Thu, 14 May 2026 18:56:06 UTC (103 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 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?)