Code Transformations Detect LLM-Written Python Across Models
ACW applies idempotent semantic-preserving Python rewrites after generation, reaching over 97% accuracy with under 0.1 seconds per function.
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ACW applies idempotent semantic-preserving Python rewrites after generation, reaching over 97% accuracy with under 0.1 seconds per function.
A structural proof shows that every poset with a planar diagram has dimension at most 96 se(P) + 672.
Controlled experiments across four UMMs find that understanding fine-tuning can improve three generation skills while avoiding direct generation shift.
Generating natural language explanations then verifying them symbolically outperforms chain-of-thought prompting.
Explicit multi-view geometry constraints improve monocular depth without any ground-truth supervision.
Random subspace methods achieve convergence rates dependent on intrinsic dimensionality, not ambient dimension.