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Machine-generated code detection is examined under out-of-distribution conditions where robust generalization is required. A hybrid feature representation is used in which code snippets are encoded through character-level TF–IDF patterns together with explicit structural indicators capturing properties such as verbosity and formatting behavior. Variability across generators is handled through clustering-based expert specialization, and predictions are produced using an ensemble of logistic regression and Naïve Bayes models with calibrated thresholds. Experimental results show that the proposed approach performs competitively despite relying on simple linear classifiers. The findings suggest that persistent structural patterns in code provide reliable cross-domain signals for identifying machine-generated programs.

ACL 2026

Stylometry at SemEval-2026 Task 13: Clustered Stylometric Modeling for Machine-Generated Code Detection

stylometry

mixture of experts

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

workshop paper

We present our systems for SemEval-2026 Task 13, built on the Droid resource suite and benchmark setting. For Subtask A (binary classification of human-written vs. machine-generated code), lexical baselines such as TF–IDF and character n-grams transferred poorly from the LeetCode training distribution to the production-code evaluation split. After correcting pipeline errors that obscured true performance and selecting stable AST features under domain shift, our final system uses 5 uncorrelated features and achieves 0.57 macro F1 on the public test set.
For Subtask C (4-way authorship classification of human, AI, hybrid, and adversarial) lexical baselines performed poorly under a significant vocabulary shift. Deep semantic models proved more promising, and a per-class weighted ensemble which included these models achieved 0.57 macro F1 on the public test set

HyperparameterOmens at SemEval-2026 Task 13: Various approaches to detecting machine- generated code

We present a hybrid system for SemEval-2026 Task 5 on graded word-sense plausibility in narrative contexts. Our approach combines prompt-based large language model (LLM) scoring with three complementary features: semantic embedding similarity, story-conditioned definition generation, and a synthetic gaze signal based on predicted fixation time. We combine these signals using an ordinary least squares regressor. On the official test set, our best system achieves 90.10 Acc±SD and 79.19 Spearman correlation. The system surpasses the reported human reference score on Acc±SD, highlighting the value of combining LLM-based judgments with targeted linguistic and cognitive-inspired features.

CiNet-Handai-Kyodai at SemEval-2026 Task 5: Combining LLM Prompting, Semantic Similarity, and Synthetic Gaze for Graded Sense Plausibility

We present a winning three-stage system for SemEval 2026 Task 12: Abductive Event Reasoning that combines graph-based retrieval, LLM-driven abductive reasoning with prompt design informed by reflective prompt evolution, and post-hoc consistency enforcement; our system ranks first on the evaluation-phase leaderboard with an accuracy score of 0.95. Cross-model error analysis across 14 models (7 families) reveals three shared inductive biases: causal chain incompleteness, proximate cause preference, and salience bias, whose cross-family convergence (51% cause-count reduction) indicates systematic rather than model-specific failure modes in multi-label causal reasoning.

AILS-NTUA at SemEval-2026 Task 12: Graph-Based Retrieval and Reflective Prompting for Abductive Event Reasoning

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.

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

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.

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