1 citations · 1 across the 2 of their papers we have counts for
5 papers
Which Changes Matter? Towards Trustworthy Legal AI via Relevance-Sensitive Evaluation and Solver-Grounded Reasoning
Chen Linze, Cai Yufan, Hou Zhe +1
Legal reasoning requires distinguishing changes that matter from those that do not. Legal AI should remain stable under legally irrelevant perturbations, but should change when per…
Towards Trustworthy Legal AI through LLM Agents and Formal Reasoning
Linze Chen, Yufan Cai, Zhe Hou +1
Legal decisions should be logical and based on statutory laws. While large language models(LLMs) are good at understanding legal text, they cannot provide verifiable justifications…
PAT-Agent: Autoformalization for Model Checking
Xinyue Zuo, Yifan Zhang, Hongshu Wang +4
Recent advances in large language models (LLMs) offer promising potential for automating formal methods. However, applying them to formal verification remains challenging due to th…
MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming
Zixiao Zhao, Jing Sun, Zhe Hou +4
With the rapid advancement of Large Language Models (LLMs), LLM-based approaches have demonstrated strong problem-solving capabilities across various domains. However, in automatic…
The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap
Yedi Zhang, Yufan Cai, Xinyue Zuo +9
Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing daily life through their exceptional language understanding and contextual generat…