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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7 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
JunoBench is the first benchmark of 111 reproducible crashes in Python ML Jupyter notebooks from Kaggle, with verified fixes and rich annotations for bug research.
CA2 integrates call stack information into RL agents for game testing and shows consistent gains over baselines that ignore code signals.
Raven automates Scratch program assessment by having instructors specify task-level video generation rules and using LLMs to analyze resulting videos for behavioral compliance, outperforming prior tools on real student submissions.
ReproScore separates readiness (26 static sub-metrics) from outcome (execution probes) and shows near-zero correlation between them on 423 repositories, validating the separation.
A responsible computing framework substitutes real protest imagery with labeled synthetic reproductions from conditional image synthesis to enable privacy-aware analysis of collective action patterns.
A literature survey on abstract concept recognition in videos that catalogs prior tasks and datasets while advocating for foundation models and reuse of decades of community experience.
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Protecting and Preserving Protest Dynamics for Responsible Analysis
A responsible computing framework substitutes real protest imagery with labeled synthetic reproductions from conditional image synthesis to enable privacy-aware analysis of collective action patterns.
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Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding
A literature survey on abstract concept recognition in videos that catalogs prior tasks and datasets while advocating for foundation models and reuse of decades of community experience.