3 papers
cs.AI2026
ReEfBench: Quantifying the Reasoning Efficiency of LLMs
Zhizhang Fu, Yuancheng Gu, Chenkai Hu +2
Test-time scaling has enabled Large Language Models (LLMs) to tackle complex reasoning, yet the limitations of current Chain-of-Thought (CoT) evaluation obscures whether performanc…
cs.AI2025
Correlation or Causation: Analyzing the Causal Structures of LLM and LRM Reasoning Process
Zhizhang FU, Guangsheng Bao, Hongbo Zhang +2
LLMs suffer from critical reasoning issues such as unfaithfulness, bias, and inconsistency, since they lack robust causal underpinnings and may rely on superficial correlations rat…
eess.AS2025
Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience
Andrew Chang, Chenkai Hu, Ji Qi +5
Group conversations over videoconferencing are a complex social behavior. However, the subjective moments of negative experience, where the conversation loses fluidity or enjoyment…