LLMs propose volatile performance improvements on real-world Java tasks that lag human developers on average, showing algorithmic benchmarks overestimate capabilities.
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MOA deploys LLM agents to detect recurring memory anti-patterns via profiling, synthesize static analyzers, and apply patches, reporting 42% heap and 11% binary-size reductions on OpenHarmony after finding over 10,000 issues.
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Do AI Models Dream of Faster Code? An Empirical Study on LLM-Proposed Performance Improvements in Real-World Software
LLMs propose volatile performance improvements on real-world Java tasks that lag human developers on average, showing algorithmic benchmarks overestimate capabilities.
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MOA: A Profiling-Guided LLM Framework for Memory-Optimization Automation at Codebase Scale
MOA deploys LLM agents to detect recurring memory anti-patterns via profiling, synthesize static analyzers, and apply patches, reporting 42% heap and 11% binary-size reductions on OpenHarmony after finding over 10,000 issues.