Larch: Learned Query Optimization for Semantic Predicates
With the advent of Large Language Models (LLMs), many database systems introduced semantic operators that enabled analytical queries over unstructured data (e.g. text, images, videos). Semantic operators typically incur high inference costs and latencies making semantic (AI) SQL queries challenging to apply on large scale datasets. At the same time, their semantic nature leads database engines to treat them as black boxes, making AISQL queries difficult to optimize. In this paper, we introduce Larch, a framework for optimizing the execution of semantic filters in AI SQL queries. Larch was inspired by two key observations: i) the high latency of semantic operators leaves significant room f...
| Authors | Fuheng Zhao, Pawel Liskowski, Zihan Li et al. |
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| Primary category | cs.DB |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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