@inproceedings{kagitha-etal-2026-unifying,
title = "Unifying Inference-Time Planning Language Generation",
author = "Kagitha, Prabhu Prakash and
Sun, Bo and
Desai, Ishan and
Zhu, Andrew and
Huang, Cassie and
Li, Manling and
Li, Ziyang and
Zhang, Li",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.415/",
pages = "8531--8574",
ISBN = "979-8-89176-395-1",
abstract = "A line of work in planning uses LLM not to generate a plan, but to generate a formal representation in some planning language, which can be input into a symbolic solver to deterministically find a plan. While showing improved trust and promising performance, dozens of recent publications have proposed scattered methods on a variety of benchmarks under different experimental settings. We attempt to unify the inference-time LLM-as-formalizer methodology for classical planning by proposing a unifying organizational framework based on intermediate representations. We thus systematically evaluate more than a dozen pipelines that subsume most existing work, while proposing novel ones that involve syntactically similar but high-resource intermediate languages (such as a Python wrapper of PDDL). We provide recipes for planning language generation pipelines, draw a series of conclusions showing the efficacy of their various components, and evidence their robustness against problem complexity."
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<abstract>A line of work in planning uses LLM not to generate a plan, but to generate a formal representation in some planning language, which can be input into a symbolic solver to deterministically find a plan. While showing improved trust and promising performance, dozens of recent publications have proposed scattered methods on a variety of benchmarks under different experimental settings. We attempt to unify the inference-time LLM-as-formalizer methodology for classical planning by proposing a unifying organizational framework based on intermediate representations. We thus systematically evaluate more than a dozen pipelines that subsume most existing work, while proposing novel ones that involve syntactically similar but high-resource intermediate languages (such as a Python wrapper of PDDL). We provide recipes for planning language generation pipelines, draw a series of conclusions showing the efficacy of their various components, and evidence their robustness against problem complexity.</abstract>
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%0 Conference Proceedings
%T Unifying Inference-Time Planning Language Generation
%A Kagitha, Prabhu Prakash
%A Sun, Bo
%A Desai, Ishan
%A Zhu, Andrew
%A Huang, Cassie
%A Li, Manling
%A Li, Ziyang
%A Zhang, Li
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F kagitha-etal-2026-unifying
%X A line of work in planning uses LLM not to generate a plan, but to generate a formal representation in some planning language, which can be input into a symbolic solver to deterministically find a plan. While showing improved trust and promising performance, dozens of recent publications have proposed scattered methods on a variety of benchmarks under different experimental settings. We attempt to unify the inference-time LLM-as-formalizer methodology for classical planning by proposing a unifying organizational framework based on intermediate representations. We thus systematically evaluate more than a dozen pipelines that subsume most existing work, while proposing novel ones that involve syntactically similar but high-resource intermediate languages (such as a Python wrapper of PDDL). We provide recipes for planning language generation pipelines, draw a series of conclusions showing the efficacy of their various components, and evidence their robustness against problem complexity.
%U https://aclanthology.org/2026.findings-acl.415/
%P 8531-8574
Markdown (Informal)
[Unifying Inference-Time Planning Language Generation](https://aclanthology.org/2026.findings-acl.415/) (Kagitha et al., Findings 2026)
ACL
- Prabhu Prakash Kagitha, Bo Sun, Ishan Desai, Andrew Zhu, Cassie Huang, Manling Li, Ziyang Li, and Li Zhang. 2026. Unifying Inference-Time Planning Language Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 8531–8574, San Diego, California, United States. Association for Computational Linguistics.