31 citations · 32 across the 8 of their papers we have counts for
9 papers
Replicate What Varies: Resource Sharing for Domain-Randomized Embodied Simulation
Zixu Hao, Weijun Wang, Xiangyu Li +4
Simulation provides a low-cost sandbox where embodied agents can attempt tasks, observe outcomes, and learn from experience. Running many diverse simulation environments concurrent…
Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation
Fu Chen, Xin Ding, Bingjia Huang +8
Generalizable embodied manipulation remains difficult to achieve through pretraining alone, due to unseen physical conditions in the real world. We argue that robots need to learn…
Zetta : An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence
Xin Ding, Liang Mi, Mingzhe Huang +12
Embodied agents are increasingly used to close the gap left by end-to-end policy models. Yet the agentic path has not realized closed-loop learning in physical execution: existing…
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
Ling Xu, Borui Li, Hao Wu +8
Embodied AI models now span vision-language-action (VLA) models and world-action models (WAMs), but practical deployment remains fragmented across model-specific Python stacks, bac…
OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism
Xiangyu Li, Huaizhi Tang, Xin Ding +3
Embodied AI agents increasingly require parallel execution of multiple tasks, such as manipulation, conversation, and memory construction, from shared observations under distinct t…
Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices
Xiangyu Li, Chengyu Yin, Weijun Wang +3
Large language models (LLMs) are increasingly deployed on edge devices. To meet strict resource constraints, real-world deployment has pushed LLM quantization from 8-bit to 4-bit,…
An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint
Yi Sun, Han Wang, Jiaqiang Li +8
Recent work has demonstrated the remarkable potential of Large Language Models (LLMs) in test-time scaling. By making models think before answering, they are able to achieve much h…
ChainStream: An LLM-based Framework for Unified Synthetic Sensing
Jiacheng Liu, Yuanchun Li, Liangyan Li +5
Many applications demand context sensing to offer personalized and timely services. Yet, developing sensing programs can be challenging for developers and using them is privacy-con…
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Yuanchun Li, Hao Wen, Weijun Wang +22
Since the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming…