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        Professor Wang Limin Receives 2025 Ant Intech Technology Award <a href="/lm/en/post/2025-09-19-wanglimin-ant-intech-award/index.html" class="font-semibold text-primary-600 dark:text-primary-300"><span class="absolute inset-0" aria-hidden="true"></span>More <span aria-hidden="true">&rarr;</span></a>
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      <p class="mt-6 text-base leading-6 text-gray-600 dark:text-gray-300">The Large Model Center conducts innovative research on large model system architectures, learning algorithms, and domain applications, providing core technologies for the next generation of artificial intelligence. The main research directions include scalable system architectures for large models, high-performance machine learning algorithms and platforms for large models, large model knowledge-enhanced learning algorithms, as well as language large models, multimodal large models, scientific large models, embodied decision-making large models, intelligent agent systems, and neural-symbolic reasoning systems, etc.</p>

      
        
        
        
        
          
        
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      <a href="/lm/en/post/2025-10-11-neurips-2025-accepted-papers/index.html"><span
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    <p class="relative z-10 mt-2 text-sm text-zinc-600 dark:text-zinc-400">9 papers from the Large Model Center at Nanjing University have been accepted to NeurIPS 2025, covering cutting-edge research in LLM continual learning, video understanding, multimodal reasoning, video generation, and molecular relational learning.</p>
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      <a href="/lm/en/post/2025-09-19-wanglimin-ant-intech-award/index.html"><span
        class="absolute -inset-x-4 -inset-y-6 z-20 sm:-inset-x-6 sm:rounded-2xl"></span><span class="relative z-10">Professor Wang Limin Receives 2025 Ant Intech Technology Award</span></a>
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    <p class="relative z-10 mt-2 text-sm text-zinc-600 dark:text-zinc-400">At the 2025 Inclusion Bund Conference, Professor Wang Limin received the 2025 Ant Intech Technology Award.</p>
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      <a href="/lm/en/post/2025-09-19-iccv2025-accepted-papers/index.html"><span
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    <p class="relative z-10 mt-2 text-sm text-zinc-600 dark:text-zinc-400">ICCV is one of the most influential top-tier conferences in computer vision. It is organized by the IEEE Computer Society and held biennially alongside CVPR and ECCV as the three flagship vision venues. ICCV covers cutting-edge topics such as image processing, object detection, 3D reconstruction, video understanding, and vision–language research, serving as a premier platform for presenting the latest advances and exchanging ideas. With its very high acceptance standards, ICCV represents the frontier trends and research hotspots of the field. Seven papers from the Large Model Center of the Department of Computer Science and Technology, Nanjing University, have been accepted to ICCV 2025.</p>
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      <a href="/lm/en/post/2025-09-19-icml2025-accepted-papers/index.html"><span
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    <p class="relative z-10 mt-2 text-sm text-zinc-600 dark:text-zinc-400">ICML is one of the most prestigious and influential conferences in machine learning. It is among the longest-running and largest venues in the field and a CCF Class-A conference. Four papers from the Large Model Center of the Department of Computer Science and Technology, Nanjing University, were accepted to ICML 2025.</p>
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      <a href="/lm/en/post/2025-04-30-cvpr25-accepted/index.html"><span
        class="absolute -inset-x-4 -inset-y-6 z-20 sm:-inset-x-6 sm:rounded-2xl"></span><span class="relative z-10">12 Papers from Nanjing University’s Large Model Center Accepted by CVPR 2025</span></a>
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    <p class="relative z-10 mt-2 text-sm text-zinc-600 dark:text-zinc-400">Twelve papers from the Large Model Center of the School of Computer Science, Nanjing University, have been accepted by CVPR 2025. The accepted works span distributed training, crowd counting, medical image segmentation, image super-resolution, and more.</p>
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        Large Language Models: A New Era of Artificial Intelligence in Language Understanding and Generation Large Language Models (LLMs) are one of the most groundbreaking technologies in the field of artificial intelligence in recent years. Trained on massive datasets, they are capable of understanding and generating natural language, demonstrating near-human-level performance in tasks such as text generation, translation, and question answering. This marks the dawn of a new era in artificial intelligence for language understanding and generation.</a></p>
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        Multimodal Large Model Research Group is committed to promoting the research and application of multimodal large models, exploring key technologies in multimodal information fusion, interaction, and reasoning, and driving the application of multimodal large models in visual, speech, text, and other multimodal data, thereby providing technical support for the development of multimodal intelligent technologies.</a></p>
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        Embodied Decision Large Model Research Group focuses on cutting-edge research in embodied intelligence, aiming to build a generalizable embodied agent through study in representation learning, policy learning, and hierarchical planning and execution.</a></p>
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        The LLM &#43; Knowledge research group has been engaged in long-term research on large language model knowledge enhancement, controllable generation, and domain-specific construction.</a></p>
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        The Large Model Learning Algorithms and Platform Research Group focuses on the construction of systems based on large models, large-scale training/inference deployment, and the application of large models. The group conducts research to address key challenges in efficient training, deployment, and the integration of domain knowledge into large models. In terms of applications, the group has a strong focus on reasoning tasks such as Automated Theorem Proving (ATP). In undergraduate education, the group offers courses on large model development, training students to build large models from scratch.</a></p>
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        ## Large Model Systems and Platforms: The Core Engine Driving the Scalable Application of Artificial Intelligence With the rapid development of large model technology, efficiently training, deploying, and managing these massive models has become a critical challenge. **Large model systems and platforms** have emerged to address this need, providing the infrastructure and toolchains necessary for the development and application of large-scale artificial intelligence models. They serve as the core engine driving the scalable application of AI. ### Core Features and Capabilities Large model systems and platforms typically offer the following core functionalities: 1. **Distributed Training**: - Supports distributed training for massive datasets and ultra-large models. - Provides efficient parallel computing and communication optimization, such as data parallelism, model parallelism, and pipeline parallelism. - Representative examples: Megatron-LM, DeepSpeed. 2. **Efficient Inference**: - Optimizes inference for large models to reduce latency and resource consumption. - Supports model compression, quantization, and acceleration techniques. - Representative examples: TensorRT, ONNX Runtime. 3. **Model Management and Deployment**: - Offers version control, monitoring, and updating capabilities for models. - Supports deployment across multiple environments, including cloud, edge, and devices. - Representative examples: MLflow, Kubeflow. 4. **Developer Tools and Ecosystem**: - Provides user-friendly APIs, SDKs, and visualization tools. - Builds open developer communities and ecosystems. - Representative examples: Hugging Face, OpenAI API. ### Representative Platforms and Systems The following are some notable large model systems and platforms: - **Hugging Face**: Offers a rich collection of pre-trained models and datasets, supporting model training, fine-tuning, and deployment. - **OpenAI API**: Provides powerful interfaces for large model services, enabling tasks like text generation and code generation. - **DeepSpeed**: Developed by Microsoft, focuses on distributed training and optimization for large-scale models. - **Colossal-AI**: Delivers efficient solutions for parallel training and inference, supporting ultra-large models. ### Future Development Trends The future development of large model systems and platforms will focus on the following directions: 1. **Performance Optimization**: Further improves training and inference efficiency while reducing resource consumption. 2. **Usability Enhancement**: Simplifies development processes and lowers the barrier to entry. 3. **Ecosystem Expansion**: Builds a more open and thriving developer ecosystem. 4. **Security and Trustworthiness**: Strengthens model security and explainability to ensure reliable applications. --- **In summary, large model systems and platforms are the critical enablers for the practical application of large model technology.** With continuous technological advancements and ecosystem improvements, they will provide stronger momentum for the scalable application of artificial intelligence, driving intelligent transformation across industries. </a></p>
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        The Scientific Large Model Research Group is dedicated to advancing interdisciplinary research in drug development, materials innovation, and energy optimization through state-of-the-art computational simulations.</a></p>
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        The Nanjing University Medical Imaging Large Model Research Group has been deeply engaged in the field of intelligent medical imaging, focusing on efficient training and low-cost fine-tuning of large models to explore cutting-edge applications in medical image segmentation, auxiliary diagnosis, and precision treatment. Facing the challenge of high annotation costs, the group is dedicated to methods such as sparse supervision, efficient data utilization, and pseudo-label optimization to reduce reliance on large-scale manual annotations while enhancing model generalization and robustness.</a></p>
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        The Edge Large Model System Research Group focuses on frontier optimization techniques for large model systems. Centered on building a high-precision, low-latency, and scalable large model service framework, our research covers operator optimization, adaptive parameter tuning, and multimodal task scheduling.</a></p>
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        The NASA research group of Nanjing University, in collaboration with renowned institutions such as Pengcheng Laboratory and Huawei Technologies Co., Ltd., has conducted comprehensive and in-depth research on key topics including large model training/inference performance and power consumption. The research achievements have not only been published at top conferences in the field of computer architecture, but have also been successfully deployed in relevant enterprises, making positive contributions to bridging the gap between theory and practice.</a></p>
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        The Cloud Large Model System Research Group is dedicated to exploring system-level performance optimization technologies for large model training, inference, and deployment in cloud environments. The team’s key research directions include: storage management optimization for cloud-based large models, efficient distribution and loading mechanisms, training process optimization strategies, and inference performance optimization technologies.</a></p>
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        The Controlled Generation Research Group has long been engaged in research related to the generation capabilities of large language models and multimodal models. Currently, the group focuses on controllable generation techniques for large models to enhance their outputs along specific attributes. Their research centers on methods such as intervention and guidance of large models, conditional control in multimodal large models, and neuron or activation localization within large models to steer their generation.</a></p>
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