2 citations · 4 across the 8 of their papers we have counts for
9 papers
AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science
An Luo, Jin Du, Xun Xian +12
Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial…
AGMA: Adaptive Gaussian Mixture Anchors for Prior-Guided Multimodal Human Trajectory Forecasting
Chao Li, Rui Zhang, Siyuan Huang +2
Human trajectory forecasting requires capturing the multimodal nature of pedestrian behavior. However, existing approaches suffer from prior misalignment. Their learned or fixed pr…
Kimi K2.5: Visual Agentic Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +333
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…
Compose Your Policies! Improving Diffusion-based or Flow-based Robot Policies via Test-time Distribution-level Composition
Jiahang Cao, Yize Huang, Hanzhong Guo +15
Diffusion-based models for robotic control, including vision-language-action (VLA) and vision-action (VA) policies, have demonstrated significant capabilities. Yet their advancemen…
Trajectory Entropy: Modeling Game State Stability from Multimodality Trajectory Prediction
Yesheng Zhang, Wenjian Sun, Yuheng Chen +4
Complex interactions among agents present a significant challenge for autonomous driving in real-world scenarios. Recently, a promising approach has emerged, which formulates the i…
An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems
Fangqiao Tian, An Luo, Jin Du +12
A multi-agent AI system (MAS) is composed of multiple autonomous agents that interact, exchange information, and make decisions based on internal generative models. Recent advances…
Safety Aware Task Planning via Large Language Models in Robotics
Azal Ahmad Khan, Michael Andrev, Muhammad Ali Murtaza +5
The integration of large language models (LLMs) into robotic task planning has unlocked better reasoning capabilities for complex, long-horizon workflows. However, ensuring safety…
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains
Xun Xian, Ganghua Wang, Xuan Bi +5
Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, financ…
MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning
Junqi Shao, Chenhao Zheng, Yuxuan Chen +2
This paper introduces MoveLight, a novel traffic signal control system that enhances urban traffic management through movement-centric deep reinforcement learning. By leveraging de…