16 citations · 17 across the 8 of their papers we have counts for
10 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…
Safe Evolution with Circuit Anchors
Yan Liu, Jie Fu, Tsung-Yi Ho
In biological evolution, unconstrained mutation can lead to catastrophic outcomes: organisms may evolve enhanced capabilities while losing essential functions for survival. Nature'…
JarvisHub: An Open Harness for Canvas-Native Multimodal Creative Agents
Yunlong Lin, Zixu Lin, Zhaohu Xing +23
Creative AI is moving from single-step asset generation toward long-horizon multimodal production. Although recent generative models can synthesize high-quality images, videos, aud…
AutoLPBench: An Auto-Generated, Agent-Friendly LP Benchmark via Inverse-KKT Construction
Shuo Ren, Yaohui Han, Yifan Shi +6
Most LP-from-text benchmarks are static datasets of word problems written and labeled by hand. Once such a dataset is released, its size is fixed, its difficulty is fixed, and ever…
AstroVLM: Expert Multi-agent Collaborative Reasoning for Astronomical Imaging Quality Diagnosis
Yaohui Han, Tianshuo Wang, Zixi Zhao +6
Vision Language Models (VLMs) have been applied to several specific domains and have shown strong problem-solving capabilities. However, astronomical imaging, a quite complex probl…
OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation
Xiaomeng Hu, Yinger Zhang, Fei Huang +7
AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety monitoring to customs import…
CARE: Decoding Time Safety Alignment via Rollback and Introspection Intervention
Xiaomeng Hu, Fei Huang, Chenhan Yuan +2
As large language models (LLMs) are increasingly deployed in real-world applications, ensuring the safety of their outputs during decoding has become a critical challenge. However,…
Echo: Simulating Distributed Training At Scale
Yicheng Feng, Yuetao Chen, Kaiwen Chen +7
Simulation offers unique values for both enumeration and extrapolation purposes, and is becoming increasingly important for managing the massive machine learning (ML) clusters and…
Elephant in the Room: Unveiling the Impact of Reward Model Quality in Alignment
Yan Liu, Xiaoyuan Yi, Xiaokang Chen +6
The demand for regulating potentially risky behaviors of large language models (LLMs) has ignited research on alignment methods. Since LLM alignment heavily relies on reward models…
Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
Yujia Zhou, Wenbo Zhang, Jingying Shao +10
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). Although existing research mainly emphasizes accu…