Zero-Shot Goal Recognition with Large Language Models
The paper discusses the evaluation of large language models (LLMs) in the context of zero-shot goal recognition. It highlights that while LLMs have shown competence in various planning tasks, their performance in goal recognition varies significantly. The findings suggest that goal recognition could serve as an important benchmark for assessing the foundational planning knowledge of LLMs.
- ▪Large language models have reached near-parity with classical planners in planning domains.
- ▪The paper presents the first systematic zero-shot evaluation of LLMs as goal recognizers on classical PDDL benchmarks.
- ▪Results indicate that LLM competence in goal recognition is uneven, with some models performing better than others based on evidence integration.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15333 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.15333 (cs) [Submitted on 14 May 2026] Title:Zero-Shot Goal Recognition with Large Language Models Authors:Kin Max Piamolini Gusmão, Nathan Gavenski, Nir Oren, Felipe Meneguzzi View a PDF of the paper titled Zero-Shot Goal Recognition with Large Language Models, by Kin Max Piamolini Gusm\~ao and Nathan Gavenski and Nir Oren and Felipe Meneguzzi View PDF HTML (experimental) Abstract: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.
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