EMO-STA evolves a shared program archive across task families then adapts candidates to targets, outperforming matched-compute single-task evolution in most of eight families while reducing overfitting on low-data tasks like ARC.
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A barrier-enforced multi-objective optimization framework for neural networks generates sharp non-crossing prediction intervals that meet exact target coverage in probabilistic forecasting.
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Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery
EMO-STA evolves a shared program archive across task families then adapts candidates to targets, outperforming matched-compute single-task evolution in most of eight families while reducing overfitting on low-data tasks like ARC.
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Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting
A barrier-enforced multi-objective optimization framework for neural networks generates sharp non-crossing prediction intervals that meet exact target coverage in probabilistic forecasting.
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