9 citations · 18 across the 30 of their papers we have counts for
21 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.…
From topology learning to graph generation: A unifying perspective
Xiaowen Dong, Hoi-To Wai, Siheng Chen +2
Learning graph structures from data is a fundamental problem that spans a wide range of signal processing and machine learning tasks. While significant effort has been made to tack…
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…
LQCDMaster: Agentic Scientific Computing for Lattice Quantum Chromodynamics Research
Haofei Gao, Tingjia Miao, Wenkai Jin +12
Lattice quantum chromodynamics (LQCD) provides a first-principles framework for computing hadronic observables, but its practical use remains limited by the substantial expertise r…
Towards Recursive Self-Evolving Agentic Literature Retrieval
Yuwen Du, Tian Jin, Jing Kang +8
Scientific literature retrieval must understand complex search intents while preserving source authenticity. Traditional keyword and embedding-based systems return authentic source…
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…
HyperTool: Beyond Step-Wise Tool Calls for Tool-Augmented Agents
Yaxin Du, Yifan Zhou, Yujie Ge +7
Tool-augmented LLM agents commonly rely on step-wise atomic tool calls, where each invocation, observation, and value transfer is exposed in the main reasoning trace. This creates…
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…
Towards Long-horizon Embodied Agents with Tool-Aligned Vision-Language-Action Models
Zixing Lei, Changxing Liu, Yichen Xiong +5
Vision-language-action (VLA) models are effective robot action executors, but they remain limited on long-horizon tasks due to the dual burden of extended closed-loop planning and…
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…
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…
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…
FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems
Wenhao Wang, Haoting Shi, Mengying Yuan +7
Training GUI agents with traditional centralized methods faces significant cost and scalability challenges. Federated learning (FL) offers a promising solution, yet its potential i…
InCoder-32B-Thinking: Industrial Code World Model for Thinking
Jian Yang, Wei Zhang, Jiajun Wu +22
Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason about hardware constraints and…
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…
TeachMaster: Generative Teaching via Code
Yuheng Wang, Runde Yang, Lin Wu +9
The scalability of high-quality online education is hindered by the high costs and slow cycles of manual content creation. Despite advancements in video generation, current approac…