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20242026
most citedPersonal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

31 citations · 32 across the 8 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.DC2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…