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ScaleDoc: Scaling LLM-based Predicates over Large Document Collections

arXiv – CS AI|Hengrui Zhang, Yulong Hui, Yihao Liu, Huanchen Zhang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

ScaleDoc is a new system that enables efficient semantic analysis of large document collections using LLMs by combining offline document representation with lightweight online filtering. The system achieves 2x speedup and reduces expensive LLM calls by up to 85% through contrastive learning and adaptive cascade mechanisms.

Key Takeaways

→ScaleDoc decouples LLM predicate execution into offline representation and online filtering phases to reduce computational costs.
→The system uses contrastive learning to train lightweight proxy models that filter documents before LLM processing.
→An adaptive cascade mechanism determines optimal filtering policies while maintaining accuracy targets.
→Testing shows 2x end-to-end speedup and up to 85% reduction in expensive LLM invocations.
→The innovation makes large-scale semantic document analysis more practical and cost-effective.

#scaledoc #llm-optimization #document-analysis #semantic-search #contrastive-learning #cascade-filtering #cost-reduction #performance-optimization

Read Original →via arXiv – CS AI

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