5 citations · 5 across the 9 of their papers we have counts for
10 papers
Long-Horizon RL Needs Muon
Kai Ruan, Jinghao Lin, Tao Chen +3
Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requires weighting trajectories o…
SPO++: Stream-Aligned Policy Optimization for Asynchronous Agentic RL
Kai Ruan, Jinghao Lin, Qianshan Wei +2
Group-relative reinforcement learning waits for sibling rollouts of the same prompt, which is costly for long and variable tool-use trajectories. Single-stream Policy Optimization…
When Does Muon Help Agentic Reinforcement Learning?
Kai Ruan, Jinghao Lin, Zihe Huang +4
Muon is competitive with AdamW in large-scale pre-training, but its operating regime in reinforcement-learning post-training remains unclear. We map this regime on ALFWorld, a spar…
Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
Kai Ruan, Zihe Huang, Ziqi Zhou +4
Large language model (LLM) agents often waste inference compute by continuing multi-step trajectories that are already doomed to fail. We study early failure prediction and inferen…
PianoFlow: Music-Aware Streaming Piano Motion Generation with Bimanual Coordination
Xuan Wang, Kai Ruan, Jiayi Han +2
Audio-driven bimanual piano motion generation requires precise modeling of complex musical structures and dynamic cross-hand coordination. However, existing methods often rely on a…
X-MoGen: Unified Motion Generation across Humans and Animals
Xuan Wang, Kai Ruan, Liyang Qian +3
Text-driven motion generation has attracted increasing attention due to its broad applications in virtual reality, animation, and robotics. While existing methods typically model h…
Benchmarking LLMs' Swarm intelligence
Kai Ruan, Mowen Huang, Ji-Rong Wen +1
Large Language Models (LLMs) show potential for complex reasoning, yet their capacity for emergent coordination in Multi-Agent Systems (MAS) when operating under strict swarm-like…
Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context
Kai Ruan, Xuan Wang, Jixiang Hong +3
While Large Language Models (LLMs) demonstrate remarkable capabilities in scientific tasks such as literature analysis and experimental design (e.g., accurately extracting key find…
Discovering physical laws with parallel symbolic enumeration
Kai Ruan, Yilong Xu, Ze-Feng Gao +4
Symbolic regression plays a crucial role in modern scientific research thanks to its capability of discovering concise and interpretable mathematical expressions from data. A key c…
Multi-frequency acousto-electromagnetic tomography
Giovanni S. Alberti, Habib Ammari, Kaixi Ruan
This paper focuses on the acousto-electromagnetic tomography, a recently introduced hybrid imaging technique. In a previous work, the reconstruction of the electric permittivity of…