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TransformerTrio at SemEval-2026 Task 13: Navigating Domain Shift and Representation Instability in Machine-Generated Code Detection

Year: 2026

Type: conference-paper

Abstract: Detecting machine-generated code is increasingly challenging due to advances in code generation models and domain variation across programming tasks.We present our submissions to SemEval-2026 Task 13, evaluating detection in three settings: binary human vs. machine classification, multi-class generator attribution, and four-way authorship classification including hybrid and adversarial cases.We compare feature-based, transformer-based, and hybrid approaches under domain shift and limited supervision.Results show that domain-specific signals often dominate model decisions, degrading generalization when training and test distributions diverge.Increasing model complexity does not consistently improve performance in low-resource or cross-domain settings and may amplify spurious correlations.These findings emphasize robustness and feature alignment over model sophistication for reliable detection.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2026.semeval-1.140

Authors: Avi Patel, Manthan Laddha, Pushti Sapovadiya, Pruthwik Mishra, Shrikant Malviya

Institution: Sardar Vallabhbhai National Institute of Technology Surat

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7166873434

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