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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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
HyPOLE introduces a HyperLTL-guided framework for partial-observability MARL integrated with CTDE, claiming advantages over baselines on SMAC, MessySMAC, and WildFire.
Ensemble voting strategies for change point detection improve F1-score by 11% over Mozilla's T-test method on a new ground-truth dataset of 174 performance time series annotated by practitioners.
citing papers explorer
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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.
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Exploring Statistical Change Point Detection Techniques for Performance Anomaly Detection at Mozilla
Ensemble voting strategies for change point detection improve F1-score by 11% over Mozilla's T-test method on a new ground-truth dataset of 174 performance time series annotated by practitioners.