Orion-MSP is a novel method introduced for tabular in-context learning, designed to handle the challenges posed by heterogeneous feature types and complex feature interactions. This approach builds upon recent advancements such as TabPFN and TabICL, integrating multi-scale sparse attention mechanisms to improve learning efficacy. Notably, Orion-MSP achieves performance levels comparable to traditional models while eliminating the need for task-specific fine-tuning, which is often a requirement in conventional approaches. The method’s ability to generalize across diverse tabular datasets without additional tuning marks a significant step forward in the field. Its development reflects ongoing efforts to enhance machine learning techniques for structured data, as documented in recent research on arXiv. Overall, Orion-MSP represents a promising advancement in tabular data modeling, offering both efficiency and competitive accuracy.