activity
20232026
most citedML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning

1 citations · 2 across the 12 of their papers we have counts for

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5 papers · 1 filter

cs.LG2026

DataMaster: Data-Centric Autonomous AI Research

Yaxin Du, Xiyuan Yang, Zhifan Zhou +12

As model families, training recipes, and compute budgets become increasingly standardized, further gains in machine learning systems depend increasingly on data. Yet data engineeri…

cs.LG2026

PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research

Tingjia Miao, Wenkai Jin, Muhua Zhang +19

The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current scientific benchmarks remain co…

cs.AI2026

EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

Xinyu Zhu, Yuzhu Cai, Zexi Liu +20

The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. However, common agent infrastructure is repeatedly rebuilt acr…

cs.LG2026

AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering

Yuzhu Cai, Zexi Liu, Xinyu Zhu +3

Autonomous Machine Learning Engineering (MLE) requires agents to perform sustained, iterative optimization over long horizons. While recent LLM-based agents show promise, current p…

cs.AI2026

Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering

Xinyu Zhu, Yuzhu Cai, Zexi Liu +8

The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic cohere…