Tailwind introduces ALPs and ML-based planning to integrate workload-specific query accelerators into standard RDBMSes, achieving 1.38x average (up to 29x) speedup on TPC-H queries.
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FM-Agent is the first framework to automate compositional Hoare reasoning for large systems by having LLMs derive natural-language function specs from caller intent and then generate tests that found 522 new bugs in systems up to 143k lines of code.
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Tailwind: A Practical Framework for Query Accelerators
Tailwind introduces ALPs and ML-based planning to integrate workload-specific query accelerators into standard RDBMSes, achieving 1.38x average (up to 29x) speedup on TPC-H queries.
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FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning
FM-Agent is the first framework to automate compositional Hoare reasoning for large systems by having LLMs derive natural-language function specs from caller intent and then generate tests that found 522 new bugs in systems up to 143k lines of code.