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20242026
most citedGTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations

3 citations · 5 across the 10 of their papers we have counts for

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cs.AI2025

LEC: Linear Expectation Constraints for Selection-Conditioned Risk Control in Selective Prediction and Routing Systems

Zhiyuan Wang, Aniri, Tianlong Chen +4

Foundation models often generate unreliable answers, while heuristic uncertainty estimators fail to fully distinguish correct from incorrect outputs, causing users to accept errone…

cs.CL2025

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees

Zhiyuan Wang, Jinhao Duan, Qingni Wang +4

Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approach…

cs.CL2025★ 1 cited

UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making

Jinhao Duan, James Diffenderfer, Sandeep Madireddy +3

As Large Language Models (LLMs) are integrated into safety-critical applications involving sequential decision-making in the real world, it is essential to know when to trust LLM d…

cs.AI2025

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction

Song Wang, Zhen Tan, Zihan Chen +3

Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existin…

cs.CL2025

SConU: Selective Conformal Uncertainty in Large Language Models

Zhiyuan Wang, Qingni Wang, Yue Zhang +4

As large language models are increasingly utilized in real-world applications, guarantees of task-specific metrics are essential for their reliable deployment. Previous studies hav…