United States

We describe our system for SemEval-2026 Task 13 on detecting machine-generated code across eight programming languages and three subtasks: binary human-vs-AI detection, 11-way source identification, and 4-way generator classification. Our approach uses a task-specific combination of Qwen2.5-Coder-1.5B with LoRA fine-tuning, abstract syntax tree (AST) features, CodeBERT with head-tail chunking, and TF-IDF features. Experiments reveal three main findings. For Task A, neural detectors degrade markedly on the official test split, while AST-based structural features remain more stable, suggesting substantial distribution shift. For Task B, inverse-frequency class weighting is essential under extreme label imbalance and greatly improves macro-F1. For Task C, combining neural and statistical models performs better than relying on a single model alone, indicating complementary strengths across representations. Our final system achieves 0.638 macro-F1 on Task A, 0.449 macro-F1 on Task B, and 0.714 macro-F1 on Task C, offering practical insights into robustness, imbalance handling, and model complementarity for AI-generated code detection.

ACL 2026

Team Yuvan at SemEval-2026 Task 13: Task-Adaptive Ensemble Strategies for AI-Generated Code Detection

machine-generated code

ai-generated code detection

lora fine-tuning

ensemble learning

class imbalance

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workshop paper

We describe our submission to SemEval-2026 Task 13, addressing binary detection 
(Subtask A), generator attribution (Subtask B), and hybrid/adversarial authorship 
classification (Subtask C) of machine-generated code (MGC). For Subtask A, we 
fine-tune two CodeBERT models with complementary sampling strategies and apply 
percentile-based post-hoc calibration, improving Macro-F1 from 0.47 to 0.56 without 
additional training. For Subtask B, we combine TF-IDF n-grams, frozen CodeBERT 
embeddings, and language features with XGBoost, using synthetic augmentation and 
class weighting to handle an 11-class dataset skewed 88% toward the human class, 
achieving Macro-F1 of 0.289. For Subtask C, we fine-tune a CodeBERT classifier for 
four-way authorship classification, achieving Macro-F1 of 0.49. Our results highlight 
the importance of probability calibration for binary detection and class balancing for 
multi-class attribution.

Bitzkrieg at SemEval-2026 Task 13: Calibration-Aware Dual CodeBERT for Multilingual Machine-Generated Code Detection

Detecting machine-generated code is crucial for maintaining software security and quality. Traditional approaches often rely on stylistic or statistical features, which are increasingly circumvented by advanced code generation models. This paper introduces a novel approach leveraging Graph Neural Networks (GNNs) to capture the structural characteristics of code, representing it as a program dependency graph. We demonstrate that our GNN-based classifier outperforms both traditional and embedding based methods on benchmark datasets, achieving improved accuracy and robustness in identifying code produced by various generation techniques. This work highlights the potential of GNNs for a more structural understanding of code authorship.

Team Poznan at SemEval-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios

We present a training-free hybrid retrieve-
then-rerank system for multi-turn retrieval-
augmented generation, submitted to all
three subtasks of SemEval-2026 Task 8
(MTRAGEval): passage retrieval (Task A),
generation with reference passages (Task B),
and end-to-end RAG (Task C). Our system ad-
dresses the core multi-turn challenges—non-
standalone questions, unanswerable queries,
and shifting passage relevance—across four
domain-specific corpora: ClapNQ, Cloud,
FiQA, and Govt. Queries are reformulated
through LLM-driven rewriting, decomposition
into sub-queries, and Hypothetical Document
Embeddings (HyDE). Retrieved candidates
from dense vector search (BGE-base-en-v1.5)
and BM25 lexical matching are fused via Re-
ciprocal Rank Fusion and reranked by a cross-
encoder (BGE-reranker-large). Llama-3.3-70B-
Instruct generates extractive, context-grounded
responses with built-in abstention for unanswer-
able queries. Using only open-source mod-
els without fine-tuning, the system achieves
nDCG@5 of 0.4098 on Task A (22nd/38), a
harmonic mean of 0.7462 on Task B (9th/26),
and 0.5796 on Task C (2nd/29), coming within
1.1% of the top submission. We attribute the
strong Task C result to the synergy between
multi-signal query reformulation and faithful
extractive generation.

Howard University-AI4PC at SemEval-2026 Task 8: Query Reformulation and Dense-Lexical Retrieval Fusion for Multi-Turn Retrieval-Augmented Generation

Large language models (LLMs) are increasingly used across diverse linguistic and cultural contexts, yet their cultural knowledge remains uneven across regions and languages. We present the DFKI-MLT system for SemEval-2026 Task 7 on cultural awareness, where we apply activation steering to multilingual LLMs using language vectors extracted from parallel FLORES data. Our method performs inference-time adaptation by adding language-specific steering vectors to the residual stream at a selected transformer layer, without any parameter updates. We participated in both the short-answer (SAQ) and multiple-choice (MCQ) tracks; however, only our MCQ submission received an official score. In the official MCQ track, we achieved 86.96% accuracy, ranking 7th out of 17 teams. To better understand system behavior, we conduct post-hoc analyses on the shared-task MCQ and SAQ settings. These analyses show that activation steering yields modest and heterogeneous improvements on cultural reasoning: gains are strongly layer-sensitive, vary substantially across language–region pairs (some configurations even degrade performance), and interact with prompt formulation (generic vs. culturally conditioned prompts). Our findings suggest that prompt design and activation steering should be jointly optimized for culturally aware multilingual inference. We release our code and experimental configurations at https://github.com/Yusser96/SemEval-2026-Track7.

DFKI-MLT at SemEval-2026 TASK 7: Steering Multilingual Models Towards Cultural Knowledge

This paper presents the Georgetown University NLP (GUNLP) system developed for SemEval 2026 Task 10: Psycholinguistic Conspiracy Marker Extraction and Detection, addressing the classification of conspiratorial beliefs in Reddit posts (Subtask 2). Our approach leverages COVID-Twitter-BERT v2 (CT-BERT-v2) within a multi-task learning framework that jointly optimizes conspiracy classification and emotion label prediction through a dual-head architecture. To address data scarcity, we enrich the training set using paraphrasing-based data augmentation and GPT-5-generated chain-of-thought emotion annotations, effectively doubling the training corpus to approximately 8,600 examples. We evaluate two input configurations: text only and text concatenated with emotion labels. The emotion-aware configuration achieves the strongest performance with an F1 score of 0.87 on the official development set, outperforming the text-only baseline by five F1 points and demonstrating the value of paraphrased samples and affective auxiliary supervision for conspiracy detection in social media text.

GUNLP at SemEval-2026 Task 10: Psycholinguistic Conspiracy Marker Extraction and Detection (PsyCoMark)

The rise of code-generating LLMs such as DeepSeek, Qwen, and Meta-LLaMA has improved developer productivity but also increased risks of plagiarism, copyright misuse, and insecure machine-generated code. While AI-text detection is well studied, machine-generated source-code detection especially across multiple languages, LLM families, and OOD conditions-remains underexplored. SemEval-2026 Task 13 addresses this via two subtasks: (A) binary human–machine code detection and (B) multi-class authorship attribution across ten LLM families. For Subtask A, we fine-tune RoBERTa, CodeBERT, GraphCodeBERT, and StarCoderBase-1B, introducing a stratified sampling strategy with class-weighted loss to mitigate imbalance and OOD shifts. For Subtask B, we mitigate the extreme human-class imbalance using undersampling, inverse-frequency weights, syntactic noising, and curriculum-based dual-path training with TinyStarCoderPy and CodeBERT. Both results show that long-context modeling, distribution-aware sampling, and noise-robust training are crucial for reliable in real-world settings. Overall, long-context modeling, distribution-aligned sampling, and lightweight noise-robust training emerge as key factors for reliable machine-generated code detection and authorship attribution.

Osint at SemEval-2026 Task 13: A Distribution-Aware Framework for Machine-Generated Code Detection and Multi-Source Authorship Attribution

This paper describes the system submitted by team Archaeology to SemEval-2026 Task~13 on AI-generated code detection. The shared task consists of three subtasks; we participate in Subtask-A (binary classification: human-written vs.\ AI-generated code) and Subtask-B (11-class attribution of the generating model).
Starting from a TF-IDF and Logistic Regression baseline, we fine-tune four pre-trained code models (CodeBERT, GraphCodeBERT, UniXcoder, and CodeT5+) with separate strategies for each subtask.
For Subtask-A, we use leave-one-language-out cross-validation, code augmentation, chunked inference with trimmed-mean aggregation, and threshold calibration on a difficult dataset.
For Subtask-B, we use sandwich token packing, class-balanced loss, and multi-seed ensembling with test-time augmentation. Our best submissions obtain macro-F1 scores of 0.737 on Subtask-A and 0.422 on Subtask-B.

Archaeology at SemEval-2026 Task 13: Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection

We describe our system for SemEval-2026 Task 13, Subtask B, which focuses on multi-class authorship attribution for code: given a code snippet, the goal is to predict whether it is human-written or generated by one of ten LLM families. The task presents two central challenges: severe class imbalance and long input sequences that frequently exceed the context length of encoder-based Transformers. To address these issues, we adopt a window-based fine-tuning and inference framework. During training, we randomly sample 512-token windows from each snippet and optimize a class-weighted cross-entropy objective with label smoothing. At inference time, we apply a sliding-window strategy and aggregate window-level logits to obtain a snippet-level prediction. We fine-tune three pretrained code encoders (CodeBERT, UniXcoder, and StarEncoder) under this framework and combine their outputs via majority voting. On the official validation split, our best single model (StarEncoder) achieves 0.60 macro F1. On the final test set, the three-model ensemble reaches 0.41 macro F1, ranking 10th on the leaderboard. Our results demonstrate that window-based modeling combined with imbalance-aware optimization provides a robust and reproducible baseline for multi-class LLM attribution under distribution shift.

ICI Innolabs at SemEval-2026 Task 13: Sliding Windows Meet Code Transformers

In this paper, we present the solution submitted by TUCNLP at SemEval-2026 Task~11: Disentangling Content and Formal Reasoning in Large Language Models. The task requires predicting the formal validity of categorical syllogisms while minimizing susceptibility to content-driven biases in English and 11 additional languages. We show that a modestly-sized model (Qwen3-8B) can achieve near-perfect logical reasoning on the English validity-only subtask, and large reductions in content effect on multilingual and premise-retrieval variants, when augmented with a multi-stage neuro-symbolic pipeline: LLM-based content stripping with iterative error correction converts natural language to abstract categorical forms, a classical symbolic parser validates against the twenty-four Aristotelian syllogistic forms, and asymmetric confidence thresholds mediate between symbolic and neural decisions. Across the four subtasks (ST1 to ST4), our system achieves accuracy ranging from 91.1\% to 100\% and bias-penalized ranking scores ($\mathcal{M}$) from 31.8 to 100.0, with the main bottleneck being overconfident neural predictions that bypass symbolic verification.

TUCNLP at SemEval-2026 Task 11: Neuro-Symbolic Content Stripping for Debiased Syllogistic Reasoning

We describe our system for SemEval-2026 Task 9 (POLAR), a shared task on detecting multilingual, multicultural, and multievent online polarization. POLAR includes three subtasks: polarization detection (ST1), polarization type classification (ST2), and polarization manifestation identification (ST3). We participated in ST1 and ST2 for Arabic. We compare three method families, encoder fine-tuning, zero-shot prompting, and retrieval-augmented in-context learning (RAG-ICL), across six LLMs and six Arabic encoder models. For ST1, RAG-ICL with Gemma-3-27B reaches F1\textsubscript{macro}~=~0.83 on the test set, a $+0.33$ absolute gain over zero-shot prompting. For ST2, a pipeline that uses the best ST1 encoder as a hard filter before RAG-ICL classification reduces hallucination from 58\% to 19\% and yields F1\textsubscript{macro}~=~0.62. English prompts consistently outperform Arabic prompts, and advanced prompting strategies (chain-of-thought, self-refinement) do not improve over standard RAG-ICL.

NAMAA at SemEval-2026 Task 9: Comparing Generative, Retrieval-Augmented, and Discriminative Methods for Arabic Online Polarization Detection and Type Classification

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