1 citations · 3 across the 11 of their papers we have counts for
4 papers · 1 filter
Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering
Junlin Yang, Che Jiang, Yu Fu +21
Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed…
NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers?
Yuru Wang, Lejun Cheng, Yuxin Zuo +14
We introduce NatureBench, a cross-discipline benchmark of 90 tasks distilled from peer-reviewed Nature-family publications, designed to evaluate whether AI coding agents can move b…
What is the best model? Application-driven Evaluation for Large Language Models
Shiguo Lian, Kaikai Zhao, Xinhui Liu +5
General large language models enhanced with supervised fine-tuning and reinforcement learning from human feedback are increasingly popular in academia and industry as they generali…
CHiSafetyBench: A Chinese Hierarchical Safety Benchmark for Large Language Models
Wenjing Zhang, Xuejiao Lei, Zhaoxiang Liu +5
With the profound development of large language models(LLMs), their safety concerns have garnered increasing attention. However, there is a scarcity of Chinese safety benchmarks fo…