EnumGRPO is a self-improving optimizer for agentic query execution that reduces LLM-operator costs by ~317x while improving accuracy by 18% over a hybrid baseline across four databases.
Lero: A Learning-to-Rank Query Optimizer
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
years
2026 9verdicts
UNVERDICTED 9representative citing papers
New Text-to-Big SQL metrics show that LLM agents must balance accuracy with cost and speed at scale, where GPT-4o trades some accuracy for up to 12x speedup and GPT-5.2 proves more cost-effective than Gemini 3 Pro on large inputs.
StructuredSemanticSearch uses table discovery operators and orientation-aware integration on model-card tables to improve evidence coverage and diversity in model recommendation queries over a semantic baseline.
Co-evolving LLM-generated solutions with their evaluators enables discovery of novel database algorithms that outperform state-of-the-art baselines, including a query rewrite policy with up to 6.8x lower latency.
Synthetic data generation exhibits disparate impact from group-specific approximation, sampling, and estimation errors; group-wise models improve both utility and parity on graphical model methods.
AutoPilot uses decentralized reinforcement learning to continuously adjust BFT protocol parameters online, achieving 49.8% lower end-to-end latency than static defaults in dynamic environments.
Proposes a formal DP-compatible framework with three unfairness measures (mutual information with TV proxy, MaxSAT-based repair, top-k tuple contribution) that satisfy positivity, monotonicity, and computability.
RELOAD achieves up to 2.4x higher robustness and 3.1x greater efficiency than prior RL-based query optimizers on Join Order Benchmark, TPC-DS, and Star Schema Benchmark.
The paper identifies gaps in LLM spatial reasoning and advocates graph-enhanced approaches for future spatial search systems.
citing papers explorer
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Cost-Aware Optimization for Agentic Query Execution
EnumGRPO is a self-improving optimizer for agentic query execution that reduces LLM-operator costs by ~317x while improving accuracy by 18% over a hybrid baseline across four databases.
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Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"?
New Text-to-Big SQL metrics show that LLM agents must balance accuracy with cost and speed at scale, where GPT-4o trades some accuracy for up to 12x speedup and GPT-5.2 proves more cost-effective than Gemini 3 Pro on large inputs.
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Diversed Model Discovery via Structured Table Discovery
StructuredSemanticSearch uses table discovery operators and orientation-aware integration on model-card tables to improve evidence coverage and diversity in model recommendation queries over a semantic baseline.
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AI-Driven Research for Databases
Co-evolving LLM-generated solutions with their evaluators enables discovery of novel database algorithms that outperform state-of-the-art baselines, including a query rewrite policy with up to 6.8x lower latency.
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Disparate Impact in Synthetic Data Generation
Synthetic data generation exhibits disparate impact from group-specific approximation, sampling, and estimation errors; group-wise models improve both utility and parity on graphical model methods.
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AutoPilot: Learning to Steer High Speed Robust BFT
AutoPilot uses decentralized reinforcement learning to continuously adjust BFT protocol parameters online, achieving 49.8% lower end-to-end latency than static defaults in dynamic environments.
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Measuring Database Unfairness via Dependency Quantification Under Differential Privacy
Proposes a formal DP-compatible framework with three unfairness measures (mutual information with TV proxy, MaxSAT-based repair, top-k tuple contribution) that satisfy positivity, monotonicity, and computability.
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RELOAD: A Robust and Efficient Learned Query Optimizer for Database Systems
RELOAD achieves up to 2.4x higher robustness and 3.1x greater efficiency than prior RL-based query optimizers on Join Order Benchmark, TPC-DS, and Star Schema Benchmark.
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Graph-Enhanced Large Language Models for Spatial Search
The paper identifies gaps in LLM spatial reasoning and advocates graph-enhanced approaches for future spatial search systems.