9 citations · 18 across the 30 of their papers we have counts for
14 papers · 1 filter
Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench
Yuxiao Hu, Qian Li, Ding Wang +4
Most symbolic regression methods optimize candidate equations, but improving an equation's fit does not establish whether its underlying scientific conjecture should be retained, r…
AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents
Xiangrui Liu, Shuo Tang, Siheng Chen
Training business automation agents requires executable tasks that connect a natural-language request to an application state and a verifiable end state. Traditional Rubrics requir…
WorkGenesis: Building the Worlds That Teach Agents to Work
Xinyu Zhu, Fenyi Liu, Yuzhu Cai +4
The ability of Large Language Model (LLM) agents to complete daily and professional work is receiving increasing attention. Training such agents requires realistic work scenarios.…
ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment
Yijun Lu, Rui Ye, Jiajun Wang +4
Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer. However, existing methods for…
Agentic Time Machine as an Infrastructure for Future-Event Forecasting
Jingyi Chai, Bingyang Zheng, Xiangrui Liu +5
Forecasting future events is a critical challenge for large language model (LLM) agents, spanning domains from elections and monetary policy to financial markets. However, evaluati…
MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation
Wenhao Wang, Peizhi Niu, Gongyi Zou +9
The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly ado…
MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection
Haowen Wang, Yaxin Du, Jian Yang +9
Mid-training has become an important stage in modern LLM development, using large-scale curated mixtures to strengthen capabilities before final post-training. Its data selection p…
LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents
Yijun Lu, Rui Ye, Yuwen Du +3
Long-horizon search agents must manage a rapidly growing working context as they reason, call tools, and observe information. Naively accumulating all intermediate content can over…
OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories
Yuwen Du, Rui Ye, Shuo Tang +4
Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The t…
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…
OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data
Yuwen Du, Rui Ye, Shuo Tang +4
Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet the development of high-performance search agents remains domin…
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…
BrowseMaster: Towards Scalable Web Browsing via Tool-Augmented Programmatic Agent Pair
Xianghe Pang, Shuo Tang, Rui Ye +3
Effective information seeking in the vast and ever-growing digital landscape requires balancing expansive search with strategic reasoning. Current large language model (LLM)-based…
X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMs
Rui Ye, Xiangrui Liu, Qimin Wu +4
LLM-based multi-agent systems (MAS) extend the capabilities of single LLMs by enabling cooperation among multiple specialized agents. However, most existing MAS frameworks rely on…