41 citations · 57 across the 20 of their papers we have counts for
24 papers
RobotWorld: Benchmarking Multimodal Agents for Robot Use Across Diverse Tasks and Embodiments
Zhiqin Yang, Chenxin Li, Xiaomeng Hu +30
General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carry into the physical world. To…
Tool-Genesis: A Task-Driven Tool Creation Benchmark for Self-Evolving Language Agent
Bowei Xia, Mengkang Hu, Shijian Wang +5
Research on self-evolving language agents has accelerated, drawing increasing attention to their ability to create, adapt, and maintain tools from task requirements. However, exist…
Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback
Mengkang Hu, Bowei Xia, Yuran Wu +9
Symbolic world models (e.g., PDDL domains or executable simulators) are central to model-based planning, but training LLMs to generate such world models is limited by the lack of l…
AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs
Yao Lai, Souradip Poddar, Sungyoung Lee +5
Despite recent advances, analog front-end design still relies heavily on expert intuition and iterative simulations, which limits the potential for automation. We present AnalogCod…
HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and Decision in Embodied Agents
Yibin Liu, Zhixuan Liang, Zanxin Chen +7
Recent advances in multimodal large language models (MLLMs) have enabled richer perceptual grounding for code policy generation in embodied agents. However, most existing systems l…
OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
Junting Chen, Haotian Liang, Lingxiao Du +8
The rapid progress of navigation, manipulation, and vision models has made mobile manipulators capable in many specialized tasks. However, the open-world mobile manipulation (OWMM)…
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Mengkang Hu, Yuhang Zhou, Wendong Fan +13
Large Language Model (LLM)-based multi-agent systems show promise for automating real-world tasks but struggle to transfer across domains due to their domain-specific nature. Curre…
RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
Tianxing Chen, Zanxin Chen, Baijun Chen +23
Simulation-based data synthesis has emerged as a powerful paradigm for advancing real-world robotic manipulation. Yet existing datasets remain insufficient for robust bimanual mani…
Truly Assessing Fluid Intelligence of Large Language Models through Dynamic Reasoning Evaluation
Yue Yang, MingKang Chen, Qihua Liu +9
Recent advances in large language models (LLMs) have demonstrated impressive reasoning capacities that mirror human-like thinking. However, whether LLMs possess genuine fluid intel…
X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System
Peng Wang, Ruihan Tao, Qiguang Chen +2
Recently, large language model (LLM)-based agents have achieved significant success in interactive environments, attracting significant academic and industrial attention. Despite t…
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
Qiguang Chen, Libo Qin, Jinhao Liu +7
Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like math…
Text2World: Benchmarking Large Language Models for Symbolic World Model Generation
Mengkang Hu, Tianxing Chen, Yude Zou +7
Recently, there has been growing interest in leveraging large language models (LLMs) to generate symbolic world models from textual descriptions. Although LLMs have been extensivel…
Electronic Circuit Principles of Large Language Models
Qiguang Chen, Libo Qin, Jinhao Liu +6
Large language models (LLMs) such as DeepSeek-R1 have achieved remarkable performance across diverse reasoning tasks. To uncover the principles that govern their behaviour, we intr…
EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents
Junting Chen, Checheng Yu, Xunzhe Zhou +7
Heterogeneous multi-robot systems (HMRS) have emerged as a powerful approach for tackling complex tasks that single robots cannot manage alone. Current large-language-model-based m…
HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model
Mengkang Hu, Tianxing Chen, Qiguang Chen +3
Large Language Model (LLM)-based agents exhibit significant potential across various domains, operating as interactive systems that process environmental observations to generate e…
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation
Mengkang Hu, Pu Zhao, Can Xu +5
Large Language Model-based agents have garnered significant attention and are becoming increasingly popular. Furthermore, planning ability is a crucial component of an LLM-based ag…
DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning
Zeyu Gao, Yao Mu, Jinye Qu +7
Dual-arm robots promise greater efficiency but require planning for complex tasks with nonlinear sub-task dependencies. Current methods using Large Language Models (LLMs) suffer fr…
Needle In A Multimodal Haystack
Weiyun Wang, Shuibo Zhang, Yiming Ren +13
With the rapid advancement of multimodal large language models (MLLMs), their evaluation has become increasingly comprehensive. However, understanding long multimodal content, as a…
AnalogCoder: Analog Circuit Design via Training-Free Code Generation
Yao Lai, Sungyoung Lee, Guojin Chen +4
Analog circuit design is a significant task in modern chip technology, focusing on the selection of component types, connectivity, and parameters to ensure proper circuit functiona…
KET-QA: A Dataset for Knowledge Enhanced Table Question Answering
Mengkang Hu, Haoyu Dong, Ping Luo +2
Due to the concise and structured nature of tables, the knowledge contained therein may be incomplete or missing, posing a significant challenge for table question answering (Table…
RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis
Yao Mu, Junting Chen, Qinglong Zhang +16
Robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI. Despite successe…
Tree-Planner: Efficient Close-loop Task Planning with Large Language Models
Mengkang Hu, Yao Mu, Xinmiao Yu +7
This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on…
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought
Yao Mu, Qinglong Zhang, Mengkang Hu +7
Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work…
TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data
Fan Zhou, Mengkang Hu, Haoyu Dong +3
Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstr…