Pixel Phantoms at SemEval-2026 Task 13: Exploring Classical and Neural Approaches for AI-Generated Code Detection
Year: 2026
Type: conference-paper
Abstract: This paper describes our system for SemEval-2026 Task 13, Subtask A: detecting whether a given code snippet is AI-generated or humanwritten.We explored a range of approaches from classical machine learning baselines using TF-IDF representations to fine-tuned transformer models pre-trained on code, specifically CodeBERT and GraphCodeBERT.Our experiments revealed a notable degradation in model performance when CodeBERT was trained beyond an optimal number of steps, indicating that continued training within an epoch leads to overfitting or representation drift.GraphCode-BERT, by contrast, yielded our best submission with a macro F1 score of 0.36866.Our findings highlight the sensitivity of code-specific transformers to training duration and suggest that early checkpoint selection is critical for this task.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166796256