Author

Haochen Zhang

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Preprint Jul 2026

SQL-RewriteBench: A Correctness-Gated, Full-Denominator Benchmark for Statement-Level SQL Rewriting [Experiment,Analysis&Benchmark]

Statement-level SQL rewriting can improve query performance and maintainability without changing the DBMS kernel, but existing benchmarks do not evaluate rewrite methods as deployable systems. They typically focus on DBMS performance, rule regression, query equivalence, or dialect translation, while missing the full path from accepting an input query to producing an executable, result-consistent, and operationally useful rewrite. We present SQL-RewriteBench, a benchmark for statement-level SQL rewriting that applies correctness gating and full-denominator accounting. Its metric suite explicitly separates Source Acceptance, Generation Rate, Execution Coverage, Result Consistency, UnsafeRewrite Rate, and speedup distribution. It also defines SCS, a deterministic index of static SQL structure, and CGOQ, a correctness-gated optimization-quality score that gives optimization credit only after the case-specific Checker Contract is satisfied. CGOQ combines runtime improvement with structural simplification through a continuous scoring function, making it suitable for deployment-oriented rewrite assessment. As an artifact, SQL-RewriteBench provides 180 executable Benchmark Instances organized into EQUIV, PERF, ROBUST, and DIALECT pools, each packaged with SQL, schema metadata, provenance, evidence, and rewrite-opportunity documentation. Across seven representative academic and LLM-based methods, every full-benchmark CGOQ is negative. Existing methods often fail before rewriting, fail result checks, or return correct rewrites that are slower or no better than the input. These results show that deployable SQL rewrite requires broader input handling, result validation, and benefit-aware rewrite decisions.

Jiang Long, Tianci Gao, Shiyuan Hao et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost

We study federated online reinforcement learning with linear function approximation. While recent multi-agent reinforcement learning algorithms achieve strong regret guarantees, they typically require sharing raw trajectories. This reliance incurs a communication cost that scales linearly with the number of episodes and violates the privacy constraints of federated settings. To address these limitations, we propose Fed-LSVI, the first provably efficient federated algorithm for online reinforcement learning with linear function approximation in episodic Markov decision processes. By integrating a determinant-based event-triggered synchronization with a stepwise backward update mechanism, Fed-LSVI enables agents to collaboratively learn an optimal policy by exchanging only compressed sufficient statistics. We prove that Fed-LSVI achieves a regret bound of $\widetilde{\mathcal O}(\sqrt{Md^3H^4T})$, where $d$ is the feature dimension, $H$ is the horizon length, $M$ is the number of agents, and $T$ is the number of episodes per agent, matching the best-known regret for multi-agent online reinforcement learning with linear function approximation. Moreover, by following the stringent communication and privacy constraints of the federated setting, Fed-LSVI reduces the communication cost to only logarithmic dependence on $T$, representing a significant improvement over prior methods.

Zi-Han Liang, Haochen Zhang, Lingzhou Xue · 0 citations