activity
20242026
most citedPosition: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI

2 citations · 2 across the 4 of their papers we have counts for

collaborators

10 papers

cs.SD2026

Investigation on the Robustness of Acoustic Foundation Models on Post Exercise Speech

Xiangyuan Xue, Yuyu Wang, Ruijie Yao +3

Automatic speech recognition (ASR) has been extensively studied on neutral and stationary speech, yet its robustness under post-exercise physiological shift remains underexplored.…

cs.SD2026

Edge-Cloud Collaborative Speech Emotion Captioning via Token-Level Speculative Decoding in Audio-Language Models

Xiangyuan Xue, Jiajun Lu, Yan Gao +3

Speech Emotion Captioning (SEC) leverages large audio-language models to generate rich, context-aware affective descriptions from speech. However, real-world deployment remains cha…

cs.CL2026

LatentMem: Customizing Latent Memory for Multi-Agent Systems

Muxin Fu, Xiangyuan Xue, Yafu Li +5

Large language model (LLM)-powered multi-agent systems (MAS) demonstrate remarkable collective intelligence, wherein multi-agent memory serves as a pivotal mechanism for continual…

cs.AI2025

Menta: A Small Language Model for On-Device Mental Health Prediction

Tianyi Zhang, Xiangyuan Xue, Lingyan Ruan +6

Mental health conditions affect hundreds of millions globally, yet early detection remains limited. While large language models (LLMs) have shown promise in mental health applicati…

cs.CL2025

CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards

Xiangyuan Xue, Yifan Zhou, Guibin Zhang +7

Self-evolution is a central research topic in enabling large language model (LLM)-based agents to continually improve their capabilities after pretraining. Recent research has witn…

cs.LG2025

Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning

Zelin Tan, Hejia Geng, Xiaohang Yu +14

While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement learning (RL) post-training remains largel…