Distilling foundation models for agentic what-if reasoning: cost, latency, and governance in a hybrid LLM+SLM architecture
Read the original at arxiv.org→arXiv:2609.16091v1 Announce Type: new Abstract: Tabular foundation models deliver strong zero-training predictive performance via in-context learning, but their high inference latency makes them impractical as...
Original headline: "Distilling Foundation Models for Agentic What-If Reasoning:Cost, Latency, and Governance in a Hybrid LLM+SLM Architecture"
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- Sep 16, 04:00 UTC arXiv cs.LG lead source Distilling Foundation Models for Agentic What-If Reasoning:Cost, Latency, and Governance in a Hybrid LLM+SLM Architecture