dille detects silent semantic faults in random forest ML pipelines with 91% precision via data-informed static analysis on Kaggle notebooks, finding 12-18% of scripts affected.
Towards understanding fine-grained programming mistakes and fixing patterns in data science,
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SelfHeal uses two ReAct agents and empirical fix patterns to repair bugs in LLM agents, outperforming baselines on a new 37-instance benchmark.
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Are We Lost in the Woods? Detecting Silent Semantic Faults for Random Forest Classifiers with Data-informed Static Analysis
dille detects silent semantic faults in random forest ML pipelines with 91% precision via data-informed static analysis on Kaggle notebooks, finding 12-18% of scripts affected.
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SelfHeal: Empirical Fix Pattern Analysis and Bug Repair in LLM Agents
SelfHeal uses two ReAct agents and empirical fix patterns to repair bugs in LLM agents, outperforming baselines on a new 37-instance benchmark.