9 papers
verdi: retrieval is not transfer for continual world model optimization
Junyu Wu, Shiqin Nie, Youyi Kou +9
Foundation world models have made remarkable progress in planning, simulation, and embodied intelligence. However, optimizing a pretrained world model toward a user-specified objec…
Gold Points Sniper: Self-guided Visual Reasoning in VLM for Fine-grained Action Understanding
Haodi Liu, Xinhang Yang, Kunda Yan +3
Robots operating in everyday environments must understand fine-grained human actions, intentions, and contextual cues from broad views where people occupy only small regions, a cap…
SEAGym: An Evaluation Environment for Self-Evolving LLM Agents
Congjie Zheng, Chuanyi Xue, Bin Liang +2
Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, run…
Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
Xinyu Pang, Zhanke Zhou, Xuan Li +5
Symbolic regression (SR) discovers compact mathematical expressions from data, yet recent LLM-based evolutionary methods remain sample-inefficient because they rely mainly on scala…
ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulation
Zhikang Chen, Yue Wang, Sen Cui +4
Electrocardiogram (ECG)-based models have achieved strong performance in diagnostic tasks, yet they remain limited in modeling how cardiac dynamics evolve under external interventi…
Affordance-Graphed Task Worlds: Self-Evolving Task Generation for Scalable Embodied Learning
Xiang Liu, Sen Cui, Guocai Yao +4
Training robotic policies directly in the real world is expensive and unscalable. Although generative simulation enables large-scale data synthesis, current approaches often fail t…