1 citations · 1 across the 8 of their papers we have counts for
10 papers
AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents
Xiangrui Liu, Shuo Tang, Siheng Chen
Training business automation agents requires executable tasks that connect a natural-language request to an application state and a verifiable end state. Traditional Rubrics requir…
DyCo-RL: Dynamic Cross-Modal Coordination for Visual Reasoning
Hangui Lin, Yan Shu, Zhengyang Liang +8
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a leading paradigm for enhancing visual reasoning in Multimodal Large Language Models (MLLMs). However, existin…
ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Society
Longchao Da, Mithun Shivakoti, Xiangrui Liu +3
Urban heat exposure is becoming an increasingly critical challenge due to the intensifying urban heat island effect. Fine-grained shade patterns, especially those induced by urban…
Video-Browser: Towards Open-web Agentic Video Browsing
Zhengyang Liang, Yan Shu, Xiangrui Liu +6
Autonomous agents are shifting information seeking from passive retrieval toward proactive, open-ended research, yet this progress has largely bypassed video, one of the web's rich…
TimeScope: Towards Task-Oriented Temporal Grounding In Long Videos
Xiangrui Liu, Minghao Qin, Yan Shu +5
Identifying key temporal intervals within long videos, known as temporal grounding (TG), is important to video understanding and reasoning tasks. In this paper, we introduce a new…
Video-XL-2: Towards Very Long-Video Understanding Through Task-Aware KV Sparsification
Minghao Qin, Xiangrui Liu, Zhengyang Liang +6
Multi-modal large language models (MLLMs) models have made significant progress in video understanding over the past few years. However, processing long video inputs remains a majo…
MASLab: A Unified and Comprehensive Codebase for LLM-based Multi-Agent Systems
Rui Ye, Keduan Huang, Qimin Wu +17
LLM-based multi-agent systems (MAS) have demonstrated significant potential in enhancing single LLMs to address complex and diverse tasks in practical applications. Despite conside…
Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding
Xiangrui Liu, Yan Shu, Zheng Liu +3
Despite advanced token compression techniques, existing multimodal large language models (MLLMs) still struggle with hour-long video understanding. In this work, we propose Video-X…
STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?
Yun Li, Yiming Zhang, Tao Lin +4
The use of Multimodal Large Language Models (MLLMs) as an end-to-end solution for Embodied AI and Autonomous Driving has become a prevailing trend. While MLLMs have been extensivel…
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
Rui Ye, Jingyi Chai, Xiangrui Liu +3
Federated learning (FL) enables multiple parties to collaboratively fine-tune an large language model (LLM) without the need of direct data sharing. Ideally, by training on decentr…