7 citations · 9 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis
Xiaoyang Fan, Yufan Cai, Zhe Hou +1
Clinical decision-making requires reasoning over incomplete, imprecise, and linguistically expressed patient narratives. While large language models (LLMs) excel at extracting late…
X-RAY: Mapping LLM Reasoning Capability via Formalized and Calibrated Probes
Tianxi Gao, Yufan Cai, Yusi Yuan +1
Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood. Existing evaluations largely emphasize task-level accuracy, often…
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…
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…