9 papers
Dual-Frontier: When Can an Agent Trust Its World Model?
Huatai Zhu, Qiang Chen, Ziqian Kou +5
Learned world models are becoming essential to general-purpose agents: by predicting action consequences, they support planning and decision-making while reducing reliance on costl…
FireWorldBench: Benchmarking Complex Physical World Intelligence through Coupled-Field Fire Dynamics
Qiang Chen, Hao Guo, Huatai Zhu +7
Understanding the physical world requires more than object recognition, scene description, and short-term visual prediction, as real-world physical systems involve multiple continu…
XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment
XPolicyLab Community, Tianxing Chen, Yue Chen +67
Robot policy evaluation and deployment remain fragmented by model-specific software dependencies, data representations, and runtime interfaces, so that connecting N policies to M e…
Decision-Weighted Flow Matching for Contextual Stochastic Optimization
Jize Xie, Haomiao Wu, Qiang Chen +2
Conditional generative models are increasingly used as scenario generators for stochastic optimization, but standard training objectives emphasize uniform distributional fit rather…
ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems
Zhixun Tan, Qiang Chen, Tairan Huang +2
Recent advances have improved the adaptive capabilities of LLM-based multi-agent systems (MAS) through memory-, skill-, and learning-based approaches, yet these approaches remain c…
Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection
Tairan Huang, Qiang Chen, Yili Wang +4
Graph anomaly detection aims to identify anomaly nodes in attributed graphs and plays an important role in real-world applications. However, existing graph anomaly detection method…
Tools as Continuous Flow for Evolving Agentic Reasoning
Tairan Huang, Siyu Shang, Qiang Chen +2
Large Language Models (LLMs) have demonstrated remarkable capabilities in orchestrating tools for reasoning tasks. However, existing methods rely on a step-wise paradigm that lacks…
Discover, Learn, and Reinforce: Scaling Vision-Language-Action Pretraining with Diverse RL-Generated Trajectories
Rushuai Yang, Zhiyuan Feng, Tianxiang Zhang +6
Scaling vision-language-action (VLA) model pre-training requires large volumes of diverse, high-quality manipulation trajectories. Most current data is obtained via human teleopera…
Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Training
Rushuai Yang, Hangxing Wei, Ran Zhang +8
Vision-language-action (VLA) models have shown strong generalization across tasks and embodiments; however, their reliance on large-scale human demonstrations limits their scalabil…