1 citations · 2 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale
Linfeng Zhang, Siheng Chen, Yuzhu Cai +46
AI agents are emerging as a practical way to run multi-step scientific workflows that interleave reasoning with tool use and verification, pointing to a shift from isolated AI-assi…
PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research
Tingjia Miao, Wenkai Jin, Jinxin Tan +22
Advances in LLM reasoning and tool use have enabled agentic science, yet frontier theoretical and computational physics remains challenging because research requires deep domain ex…
ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning
Zexi Liu, Yuzhu Cai, Xinyu Zhu +6
As AI capabilities advance toward and potentially beyond human-level performance, a natural transition emerges where AI-driven development becomes more efficient than human-centric…
ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering
Zexi Liu, Jingyi Chai, Xinyu Zhu +5
The emergence of large language model (LLM)-based agents has significantly advanced the development of autonomous machine learning (ML) engineering. However, the dominant prompt-ba…
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation
Shuo Tang, Xianghe Pang, Zexi Liu +6
Post-training is essential for enabling large language models (LLMs) to follow human instructions. However, its effectiveness depends on high-quality instruction data, which is cha…
Training-Free Message Passing for Learning on Hypergraphs
Bohan Tang, Zexi Liu, Keyue Jiang +2
Hypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to gen…
Hypergraph Transformer for Semi-Supervised Classification
Zexi Liu, Bohan Tang, Ziyuan Ye +3
Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities. Hypergraph neural networks emerge as a powerful tool for…