New MLS-Bench benchmark introduces 140 tasks across 12 domains to evaluate recursive AI self-improvement
The benchmark addresses a lack of standardized self-evolution testing.
The benchmark addresses a lack of standardized self-evolution testing.
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This was a large cross-domain effort, made possible by collaborators across systems, language models, robotics, AI for Science, trustworthy learning, and more. Huge thanks to everyone who helped build, validate, and test MLS-Bench: Bohan Lyu, Yucheng Yang, Siqiao Huang @KnightNemo_, Jiaru Zhang, Qixin Xu, Xinghan Li @XinghanLi66, Xinyang Han, Yicheng Zhang, Huaqing Zhang @zhqwqwq, Runhan Huang @RunhanH, Kaicheng Yang, Zitao Chen, Wentao Guo @WentaoGuo7, Junlin Yang @junlin45300, Xinyue Ai @KeelyAi04, Wenhao Chai @wenhaocha1, Yadi Cao @YadiCao, Ziran Yang @__zrrr__, Kun Wang @KunWang0129, Dapeng Jiang, Huan-ang Gao, Shange Tang @sangertang1999, Chengshuai Shi @chengshuai_shi, Simon S. Du @SimonShaoleiDu, Max Simchowitz @max_simchowitz, Jiantao Jiao @JiantaoJ, Dawn Song @dawnsongtweets, and Chi Jin @chijinML. (8/9)
