Papers by Cassie Huang
Unifying Inference-Time Planning Language Generation (2026.findings-acl)
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Prabhu Prakash Kagitha, Bo Sun, Ishan Desai, Andrew Zhu, Cassie Huang, Manling Li, Ziyang Li, Li Zhang
| Challenge: | Large language models (LLMs) are used to generate a formal representation of a plan in a planning language. |
| Approach: | They propose a unifying organizational framework based on intermediate representations to unify the inference-time LLM-as-formalizer methodology for classical planning. |
| Outcome: | The proposed framework subsumes most existing work and proposes new ones that involve syntactically similar but high-resource intermediate languages. |
On the Limit of Language Models as Planning Formalizers (2025.acl-long)
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| Challenge: | Large Language Models can create plans that are neither executable nor verifiable in grounded environments. |
| Approach: | They use Large Language Models to generate a formal representation of the planning domain in some language, such as Planning Domain Definition Language (PDDL). |
| Outcome: | The proposed model outperforms the models directly generating plans while being robust to lexical perturbation. |
Language Model as Planner and Formalizer under Constraints (2026.acl-long)
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| Challenge: | Large language models (LLMs) have been widely used in planning but lack interpretability and control. |
| Approach: | They propose to augment widely used planning benchmarks with manually annotated, fine-grained, and rich natural language constraints spanning four formally defined categories. |
| Outcome: | The proposed model outperforms existing models in 4 state-of-the-art reasoning LLMs, 4 formal languages, and 4 datasets. |