Tabular Foundation Model Performance Under Varying Pretraining-to-Finetuning Data Ratios on TabBench Tasks

Generative AI foundation models offer transformative potential for processing structured biological data, particularly in single-cell RNA sequencing, where datasets are rapidly scaling toward billions of cells. We propose the use of agentic foundation models with real-time web search to automate the labeling of experimental data, achieving up to 82.5\% accuracy. This addresses a key bottleneck in supervised learning for structured omics data by increasing annotation throughput without manual curation and human error. Our approach enables the development of virtual cell foundation models capable

Research goal: What is the impact of varying the pretraining-to-finetuning data ratio on the inference throughput and task-specific accuracy of tabular foundation models on TabBench tasks?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.1/10.