83 citations · 86 across the 10 of their papers we have counts for
10 papers
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…
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…
Efficient Multi-round LLM Inference over Disaggregated Serving
Wenhao He, Youhe Jiang, Penghao Zhao +4
With the rapid evolution of Large Language Models (LLMs), multi-round workflows, such as autonomous agents and iterative retrieval, have become increasingly prevalent. However, thi…
Hunyuan-GameCraft-2: Instruction-following Interactive Game World Model
Junshu Tang, Jiacheng Liu, Jiaqi Li +8
Recent advances in generative world models have enabled remarkable progress in creating open-ended game environments, evolving from static scene synthesis toward dynamic, interacti…
Learning from History: A Retrieval-Augmented Framework for Spatiotemporal Prediction
Hao Jia, Penghao Zhao, Hao Wu +3
Accurate and long-term spatiotemporal prediction for complex physical systems remains a fundamental challenge in scientific computing. While deep learning models, as powerful param…
BeamVQ: Aligning Space-Time Forecasting Model via Self-training on Physics-aware Metrics
Hao Wu, Xingjian Shi, Ziyue Huang +6
Data-driven deep learning has emerged as the new paradigm to model complex physical space-time systems. These data-driven methods learn patterns by optimizing statistical metrics a…
Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling
Shuaipeng Li, Penghao Zhao, Hailin Zhang +10
In current deep learning tasks, Adam style optimizers such as Adam, Adagrad, RMSProp, Adafactor, and Lion have been widely used as alternatives to SGD style optimizers. These optim…
Retrieval-Augmented Generation for AI-Generated Content: A Survey
Penghao Zhao, Hailin Zhang, Qinhan Yu +7
Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (…
Experimental Analysis of Large-scale Learnable Vector Storage Compression
Hailin Zhang, Penghao Zhao, Xupeng Miao +4
Learnable embedding vector is one of the most important applications in machine learning, and is widely used in various database-related domains. However, the high dimensionality o…
Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features
Qingrui Jia, Xuhong Li, Lei Yu +5
While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled sample…