Junming Liu

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I am currently a Research Intern at Shanghai AI Lab, where I work on topics related to Generative Intelligence, Multimodal Reasoning, and Graph Theory. Prior to this, I received my Master’s degree in Computer Science from Tongji University.

My work seeks to enhance the creative capabilities and cognitive depth of AI systems, while ensuring logical consistency through knowledge representations. Recently, my research has been centered around the following areas:

  • Generative Modeling for Scientific Discovery.
  • Memory-Augmented AI Agents.
  • Mid-training and Post-training of Multimodal Large Language Models.

I am actively seeking Research Assistant, Research Internship, and Research Collaboration. I am available for both on-site and remote opportunities. Please feel free to contact me.

News

Jun, 2026 Our paper Mixture-of-Experts with Advanced Integrated Collaboration (Mosaic) has been accepted by Knowledge-Based Systems (KBS)! Congrats to all collaborators! 🎉🎉
May, 2026 Our paper Neuroevolution for Physical Dynamics (Evo-ManiEarth) has been accepted by IEEE Transactions on Evolutionary Computation (TEVC)! Congrats to all collaborators! 🎉🎉
Apr, 2026 Our survey “A Comprehensive Survey of Interaction Techniques in 3D Scene Generation” has been accepted by IJCAI 2026! We propose a unified taxonomy covering interactive generation, interactive editing, and embodied interaction. A curated list of related papers is available at Link. 🤗🤗
Jan, 2026 Our paper Domain-Adaptive Model Merging (DMM) has been accepted by ICASSP 2026! 🎉🎉
Jan, 2026 Our paper Adversarial Mutual Information Distillation (AMID) has been accepted by WWW 2026! 🎉🎉
Nov, 2025 Our paper ReBrain has been accepted by WACV 2026! 🎉🎉
Aug, 2025 Our paper Commonality-Oriented Gradient Optimization (COGO) has been accepted by PRCV 2025! 🎉🎉
Jul, 2025 Our paper Hierarchical Multi-Agent Retrieval-Augmented Generation (HM-RAG) has been accepted by ACM MM 2025! HM-RAG orchestrates a three-tier agent hierarchy to split complex queries and unify diverse modalities, advancing multimodal RAG reasoning. Code available at Link. 🤗🤗
Jun, 2025 Our paper Vision-align-to-Language integrated Knowledge Graph (VaLiK) has been accepted by ICCV 2025! VaLiK grounds vision in text and filters noise for annotation‑free MMKGs, boosting LLM reasoning to SOTA with high efficiency. Code available at Link. 🤗🤗
Jan, 2025 Join Shanghai AI Lab as a Research Intern, targeting Knowledge Reasoning!⚡️️⚡️

Selected Publications

  1. WWW
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    AMID: Model-Agnostic Dataset Distillation by Adversarial Mutual Information Minimization
    Aoqi Wu, Junming Liu, Yuwei Zhang, Weiquan Huang, Liang Hu†, Yifan Yang, Qi Zhang, Jiaxing Miao, Yuhan Tang, and Zhongyuan Lai
    Proceedings of the ACM on Web Conference, 2026
    PDF
  2. ACM MM
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    HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation
    Pei Liu, Xin Liu, Ruoyu Yao, Junming Liu, Siyuan Meng, Ding Wang†, and Jun Ma†
    Proceedings of the 33rd ACM International Conference on Multimedia, 2025
    PDF Code
  3. ICCV
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    Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning
    Junming Liu, Siyuan Meng, Yanting Gao, Song Mao, Pinlong Cai, Guohang Yan, Yirong Chen, Zilin Bian, Ding Wang†, and Botian Shi
    Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025
    PDF Code
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