From Compression to Exploration: Active Querying Agent for 3D Physical Field Understanding
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.