3 papers
cs.AI2026
Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback
Jiamei Jiang, Jiajing Zhang, Feifei Mo +2
Planning often requires symbolic specifications that are both executable and verifiable. For large language models deployed in autonomous or decision-support systems, failures in s…
cs.AI2026
Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
Jiajing Zhang, Jiamei Jiang, Chenyang Zhang +3
Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns regarding robustness and reliabi…
cs.CL2025
Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models?
Yifei Wang, Yu Sheng, Linjing Li +1
Recent advances in handling long sequences have facilitated the exploration of long-context in-context learning (ICL). While much of the existing research emphasizes performance im…