AutoBricks: LLM-Driven Automated Brick Building for Spatio-Temporal Forecasting

Designing high-performing spatio-temporal forecasters requires expertise in matching architectures to data topology, while off-the-shelf LLMs can introduce interface errors and tensor mismatches when generating forecasting code. We address this gap through retrieval-augmented modular assembly over a standardized brick registry, with a frozen LLM proposing architectures from task descriptions and retrieved experience. Our framework, AutoBricks, provides 38 operators with standardized invocation interfaces. A dual memory mechanism retrieves successful architectural configurations and failure trajectories to guide candidate generation, while execution and validation feedback inform subsequent proposals without updating LLM parameters. Experiments across 20 spatio-temporal datasets demonstrate high code executability and up to $15.1\%$ lower MAE than the strongest baselines. AutoBricks also substantially reduces search costs relative to NAS methods. Operator-pool and random-assembly comparisons further demonstrate the effectiveness of guided assembly. Code and demo are available at https://anonymous.4open.science/r/AutoBricks/.