作者位次 #2/5ACL 2026Poster· Natural Language Processing
R e E f B ench: Quantifying the Reasoning Efficiency of LLM s
Zhizhang Fu · Yuancheng Gu · Chenkai Hu · Hanmeng Liu · Yue Zhang
摘要
Test-time scaling has enabled Large Language Models (LLMs) to tackle complex reasoning, yet the limitations of current Chain-of-Thought (CoT) evaluation obscure whether performance gains stem from genuine reasoning or mere verbosity. To address this, (1) we propose a novel neuro-symbolic framewor...