9 citations · 9 across the 16 of their papers we have counts for
Showing 2026 · cs.AIShow all
3 papers · 2 filters
cs.AI2026
GRUET: Quantifying Uncertainty of Agentic Reasoning-and-Acting Processes
Shuang Liang, Xin-Yu Hu, Shao-Qun Zhang
Agents have attracted considerably increasing attention due to the power of executing both Reasoning and Acting (ReAct) in open and dynamic environments. The ReAct process typicall…
cs.AI2026
GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity
Shuang Liang, Xin-Yu Hu, Xiang-Jun Ou +1
Recent years have witnessed great advances in the reasoning ability of Large Language Models (LLMs). However, the reasoning processes of LLMs often exhibit uncertainty, where LLMs…
cs.AI2026
The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models
Xiang-Jun Ou, Shuang Liang, Xin-Yu Hu +3
Recent advancements in Large Language Models (LLMs) have enabled sophisticated reasoning and content generation, yet their inherent stochasticity poses significant challenges for e…