For the test to be fair for LLMs, the SAT instance should be reasonably large, but not too big. I can't just give SAT problems with thousands of variables. But also it shouldn't be too easy.
MiniMax 上线 MaxClaw 功能
。关于这个话题，51吃瓜提供了深入分析
他在演說中向在場的軍事英雄與奧運金牌冰球選手致敬，或許暗示這是他的一場政治賭博。
Петербург приблизился к новому метеорекордуСиноптик Леус: Петербург пережил один из самых длинных безоттепельных периодов
Can these agent-benchmaxxed implementations actually beat the existing machine learning algorithm libraries, despite those libraries already being written in a low-level language such as C/C++/Fortran? Here are the results on my personal MacBook Pro comparing the CPU benchmarks of the Rust implementations of various computationally intensive ML algorithms to their respective popular implementations, where the agentic Rust results are within similarity tolerance with the battle-tested implementations and Python packages are compared against the Python bindings of the agent-coded Rust packages: