collaborators

7 papers

cs.LG2026

Intern-S2-Preview: Scientific Agentic Foundation Model

Lei Bai, Jiaqi Cao, Chiyu Chen +121

Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sus…

cs.SC2026

Symbolic-Neural Soft-Logic Reasoning: Towards Robust and Verifiable Thinking Chains via Cooperative Evolution

Rui Wang, Zeming Wei, Yihao Zhang +1

Large Language Models (LLMs) have demonstrated impressive progress in complex reasoning tasks, largely driven by the Chain-of-Thought (CoT) paradigm, which decomposes difficult pro…

eess.AS2026

StepAudio 2.5 Technical Report

Bin Lin, Bo Zhao, Boyong Wu +98

Unified audio-language modeling has emerged as a prominent trend in modern speech systems, promising to bring the reasoning capabilities of large language models to auditory tasks.…

cs.CL2026

LLMs for automatic annotation of Mandarin narrative transcripts

Qingwen Zhao, Hongao Zhu, Yunqi He +3

Linguistic annotation of transcribed speech is essential for research in language acquisition, language disorders, and sociolinguistics, yet remains labor-intensive and time-consum…

cs.LG2026

Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale

Yicheng Zou, Dongsheng Zhu, Lin Zhu +174

We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…

cs.AI2025

Conversational Time Series Foundation Models: Towards Explainable and Effective Forecasting

Defu Cao, Michael Gee, Jinbo Liu +4

The proliferation of time series foundation models has created a landscape where no single method achieves consistent superiority, framing the central challenge not as finding the…

cs.AI2025

Agent4S: The Transformation of Research Paradigms from the Perspective of Large Language Models

Boyuan Zheng, Zerui Fang, Zhe Xu +13

While AI for Science (AI4S) serves as an analytical tool in the current research paradigm, it doesn't solve its core inefficiency. We propose "Agent for Science" (Agent4S)-the use…