OckBench: Measuring the Efficiency of LLM Reasoning 事件

OckBench: Measuring the Efficiency of LLM Reasoning arXiv:2511.05722v3 Announce Type: replace Abstract: Large language models (LLMs) such as GPT-5 and Gemini 3 have pushed the frontier of automated reasoning and code generation. Yet current benchmarks emphasize accuracy and output quality, neglecting a critical dimension: efficiency of token usage. The token efficiency is highly variable in practical. Models solving the same problem with similar accuracy can exhibit up to a \textbf{5.0$\times$}