6 papers
TRACE: Target-State Risk-Aligned Conditional Execution for Embodied Tracking
Mingyang Lyu, Gongce Cao, Yiyang Jia +7
Embodied visual tracking requires a robot to follow a language-specified person under occlusion, look-alike distractors, and ambiguous instructions. We show that improving tracking…
SPT-Flow: One Integration, Many Futures in Continuous-Time World Models
Mingyang Lyu, Yinqian Sun, Gongce Cao +5
Continuous-time world models generate a latent trajectory through a single ODE integration, but adding stochastic conditioning does not ensure that the model uses it. In our teache…
When Truth Is Distributed: Misinformation Derails Collective Fact Recovery in LLM-Based Multi-Agent Systems
Chenfei Yan, Zeyang Yue, Feifei Zhao +6
LLM-based multi-agent systems promise effective collaborative reasoning, but communication may amplify local errors into collective risks, and while existing evaluations emphasize…
ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models
Mingyang Lyu, Yinqian Sun, Yiyang Jia +5
In embodied intelligence, safety is a prerequisite for reliable robot deployment in the physical world. Current vision-language-action (VLA) models continue to advance toward gener…
Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models
Mingyang Lyu, Yinqian Sun, Erliang Lin +4
Vision-Language-Action (VLA) models such as OpenVLA, Octo, and have shown strong generalization by leveraging large-scale demonstrations, yet their performance is still funda…
Spiking World Model with Multi-Compartment Neurons for Model-based Reinforcement Learning
Yinqian Sun, Feifei Zhao, Mingyang Lv +1
Brain-inspired spiking neural networks (SNNs) have garnered significant research attention in algorithm design and perception applications. However, their potential in the decision…