Compositional Online Learning for Semantic Data Processing Systems
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TL;DR - This paper introduces compositional online learning for semantic data systems, exploiting LLM-call latency to hide CPU-side model updates. In a Cortex AISQL case study, combined filter ordering and cascade routing reduce estimated per-row LLM cost by a realistic factor near 8×.
- Learner updates, including gradient steps and threshold optimization, run during the next LLM round-trip rather than adding latency.
- The framework organizes components by decision granularity and update cadence while sharing a common online-training pattern.
- The production design combines memoization, per-call filter ordering, and per-batch cascade routing.
- Theoretical savings reach an 11.4× upper bound under independence, reduced by practical effects such as self-selection, smaller sample budgets, and selectivity drift.
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Compositional Online Learning for Semantic Data Processing Systems
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TL;DR - This paper introduces compositional online learning for semantic data systems, exploiting LLM-call latency to hide CPU-side model updates. In a Cortex AISQL case study, combined filter ordering and cascade routing reduce estimated per-row LLM cost by a realistic factor near 8×.
- Learner updates, including gradient steps and threshold optimization, run during the next LLM round-trip rather than adding latency.
- The framework organizes components by decision granularity and update cadence while sharing a common online-training pattern.
- The production design combines memoization, per-call filter ordering, and per-batch cascade routing.
- Theoretical savings reach an 11.4× upper bound under independence, reduced by practical effects such as self-selection, smaller sample budgets, and selectivity drift.