arXiv subjects

Kenta Nanaumi

Publications and source records attributed to Kenta Nanaumi.

2 recordsLinked to original sources

arXiv2026

LLM-as-a-Judge for Contest-Style Human-AI Co-Creation: A Reliability-Aware Evaluation Framework for Coding

Large language models now judge open-ended outputs and co-create code with programmers, yet judgments of human-AI co-creation must be reliable, comparable across models, and interpretable over multi-turn interaction. This work presents a rubric-driven LLM-as-a-Judge framework for contest-style human-AI co-creation in coding, evaluated on 15 participants, 13 problems, 184 trajectories, and 517 attempts from an AI-permitted track run in parallel with a programming contest. Four judges are evaluated under grouped cross-validation with cluster-bootstrap 95% intervals and probed by injecting single defects into accepted programs against behavior-preserving controls. Three findings concern the judges as instruments and likely extend beyond this setting. First, the judge input is a first-order design variable: supplying the problem statement in place of an identifier rebuilds the judges' ranking (rank correlation 0.2617 [0.123, 0.391] for the three judges whose model was unchanged) and raises their ROC-AUC by 0.130 [0.061, 0.202] to 0.295 [0.214, 0.381]. Second, schema compliance does not imply scale validity: the judges emit as few as 8 distinct probability values, leaving as few as 6 of 99 thresholds effective. Third, accuracy and defect sensitivity are separable: Claude and Gemini are indistinguishable on accuracy (ROC-AUC 0.6985 [0.633, 0.763] against 0.7691 [0.719, 0.817]) yet separate on defect detection (0.8380 [0.806, 0.871] against 0.7512 [0.719, 0.783]). For co-creation, Success@Turn rises from 0.6033 [0.533, 0.674] at the first attempt to 0.8370 [0.783, 0.886] by the tenth, 44 of the 73 trajectories unsolved at first (60.3%) succeed later, and revision size is unrelated to judged improvement (Spearman 0.0007 [-0.085, +0.090]).

cs.SE↗

arXiv2026

Revision-Aware Success Prediction from Multi-Attempt Programming Trajectories

Programming outcome prediction plays a central role in data-driven programming education, supporting learner modeling, timely intervention, and adaptive assistance. Yet predicting submission success is difficult due to heterogeneous error states, short-term revisions, and uneven future-horizon availability in programming trajectories. This study examines three prediction tasks under a unified formulation: whether the current attempt is accepted (Task~1), whether the next attempt is accepted (Task~2), and whether acceptance is reached within a three-attempt recovery window (Task~3). Each task is evaluated across current-only, pairwise, and multi-step input regimes using ML, DL, and transformer-based pretrained models (PTM), represented by LinearSVM, XGBoost, BiGRU, BiLSTM, GraphCodeBERT, and CodeT5+. Results show a consistent pattern: the current-only regime is the most reliable, while pairwise and multi-step history provide no consistent gain. ML models are the strongest and most stable overall, particularly in Tasks~1 and~3, and Task~2 is the hardest across all model families. DL and PTMs perform well on Task~3 but are more task-dependent. In the Task~3 current-only setting, XGBoost achieves AP/PR-AUC of 99.09% and MCC of 0.6325, while GraphCodeBERT and CodeT5+ reach F1 scores of 80.00% and 73.68%, respectively. A sensitivity analysis confirms that Task~3 conclusions hold most robustly for ML models under stricter future-horizon control. Across all settings, ML models remain highly effective for programming success prediction, while complex models offer value in specific settings. This work provides a systematic comparison across predictive formulations and offers robust modeling guidance for submission-aware analytics in programming education, where near-future success prediction can inform timely intervention in online judge platforms and adaptive programming support systems.

cs.CY↗