Distilling Foundation Models for Agentic What-If Reasoning:Cost, Latency, and Governance in a Hybrid LLM+SLM Architecture

The paper presents a method for compressing large tabular foundation models into compact feed‑forward students for use in interactive agentic decision loops. By distilling a TabPFN teacher into a student, the authors reduce model size from 53.2 M to 8,546 parameters (6,220× compression) and from 111.4 M to 17,059 parameters (6,532× compression) while preserving 95.4–100.5% accuracy and 96.8–100.0% AUC across several benchmarks. The study demonstrates that soft‑target distillation yields a 2.1–7.0 AUC improvement over hard‑label training.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• llms
• agents
• efficiency
• benchmarks