Literate execution treats documentation and visualizations as dynamic, computable parts of program execution via provenance tracking, inverting traditional literate programming to make programs more explorable.
Towards a Framework for Algorithm Recognition in Binary Code
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Hybrid LLM plus static analysis for algorithm recognition in code cuts required model calls by 72-97% and lifts F1-scores by as much as 12 points.
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Literate Execution
Literate execution treats documentation and visualizations as dynamic, computable parts of program execution via provenance tracking, inverting traditional literate programming to make programs more explorable.
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Combining Static Code Analysis and Large Language Models Improves Correctness and Performance of Algorithm Recognition
Hybrid LLM plus static analysis for algorithm recognition in code cuts required model calls by 72-97% and lifts F1-scores by as much as 12 points.