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cs.AI2026
Learning to Reason with Selective Internal Feedback
Xun Lu, Shumeng Yang, Xinrui Wang +3
Reinforcement learning from internal feedback can improve language models without ground-truth answers or external verifiers. However, confidence and correctness can diverge during…
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…