Papers by Shuhui Qu
A Category-Theoretic Approach to Neural-Symbolic Task Planning with Bidirectional Search (2025.findings-emnlp)
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| Challenge: | Empirical evaluations demonstrate that our method improves completion rates by up to 6.6% and action accuracy by 9.1% . |
| Approach: | They propose a Neural-Symbolic Task Planning framework that integrates Large Language Model (LLM) decomposition with category-theoretic verification for resource-aware, temporally consistent planning. |
| Outcome: | The proposed framework improves completion rates and action accuracy by up to 6.6% . it also eliminates resource violations while ensuring resource-awareness and consistency. |