collaborators

7 papers

cs.SD2026

VoTSpeech: Decoupling Voice Design from Speech Generation in Instruction-Following TTS

Wenbing Yang, Qihang Lu, Zihan Sun +4

Instruction-following text-to-speech (TTS) typically leaves voice design implicit, as conventional speaker embeddings encode reference audio rather than text-driven targets. We pro…

cs.CL2026

SocialRL: Refining LLMs' Social Intelligence through Multi-turn Reinforcement Learning and Reward Design

Jianing Wang, Xintao Wang, Aili Chen +7

Social intelligence enables agents to read social context, infer intent, and adapt over sustained dialogue. As language models become autonomous collaborators, it is central to bui…

cs.LG2026

Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA

Mind Lab, :, Vin Bo +80

Macaron-V1 is an open agent-model family for experiential intelligence: learning from experience in real environments and continuing to learn after deployment. It is organized arou…

cs.SD2026

CapTalk: Unified Voice Design for Single-Utterance and Dialogue Speech Generation

Xiaosu Su, Zihan Sun, Peilei Jia +1

Voice design from natural language descriptions is emerging as a new task in text-to-speech multimodal generation, aiming to synthesize speech with target timbre and speaking style…

cs.SD2026

Hello-Chat: Towards Realistic Social Audio Interactions

Yueran Hou, Peilei Jia, Zihan Sun +5

Recent advancements in Large Audio Language Models (LALMs) have demonstrated exceptional performance in speech recognition and translation. However, existing models often suffer fr…

cs.CL2026

HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns

Xintao Wang, Jian Yang, Weiyuan Li +8

Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Langu…

cs.CL2025

SI-Bench: Benchmarking Social Intelligence of Large Language Models in Human-to-Human Conversations

Shuai Huang, Wenxuan Zhao, Jun Gao

As large language models (LLMs) develop anthropomorphic abilities, they are increasingly being deployed as autonomous agents to interact with humans. However, evaluating their perf…