The "2nd Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'26)" workshop focuses on advancing graph machine learning (GML) techniques in the context of large-scale foundation models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex dependencies in knowledge, systems, and behaviors. As the scale and influence of foundation models grow, graph learning is well positioned to enhance model robustness, improve interpretability, and integrate domain-specific relational priors. This workshop explores how graph learning can support emerging challenges in knowledge reasoning, temporal and multi-hop inference, and AI systems. It also investigates how advances in representation learning, structure-aware generalization, and efficient graph processing can contribute to trustworthy and scalable AI systems. By convening experts in graph learning, knowledge management, and LLMs, the workshop aims to identify core challenges and opportunities of GML in the large model era.
Qingyun Sun, Ziwei Zhang, Xingcheng Fu et al.· Proceedings of the 32nd ACM...· 0 citations
Recent advances enable LLMs to generate simulation code from natural language, yet interpreting 3D physical field outputs remains unsolved. Existing 3D scene compression methods fail on physical fields due to absent semantic grounding and information loss. We discover that typical physical fields exhibit extreme information redundancy, motivating a paradigm shift from lossy compression to selective exploration. Building on this insight, we propose AQUA, which trains agents to actively query information-rich regions through Gaussian Splatting environments, transforming global lossy compression into local lossless localization with targeted spatial queries. Agents learn query strategies via physics-guided reinforcement learning, overcoming early-stage sparsity without expert-annotated trajectories. On PhysQA-Bench, AQUA achieves 72% accuracy on average, outperforming baselines by 15% across all four datasets. Our code is available at https://github.com/gaoch6258/AQUA.git.
Chonghan Gao, Haoyi Zhou, Zhemeng Luo et al.· Proceedings of the 32nd ACM...· 0 citations
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