MindFlayer at SemEval-2026 Task 13:LACR-ENS: Calibration-Aware Ensemble Routing for Cross-Language AI-Generated Code Detection

AbstractThis paper presents LACR-ENS, a calibration-aware ensemble system for detecting AI-generated code across eight programming languages (SemEval-2026 Task 13). We identify a severe asymmetric out-of-distribution (OOD) failure in fine-tuned code transformers: Expected Calibration Error doubles from 0.09 (seen languages) to 0.18 (unseen languages), and high-confidence predictions (p0.80) are wrong 39% of the time on OOD inputs. We propose Language-Aware Confidence Routing (LACR), formally equivalent to implicit per-language temperature scaling, which reduces OOD ECE to 0.11 and improves macro-F1 by +0.013 over fixed-threshold ensembling. A language-family proximity analysis reveals that syntactic distance to training languages predicts OOD F1 with Pearson r=+0.94, providing a principled, label-free signal for deployment risk assessment and motivating a continuous routing extension. Our system combines UniXCoder and GraphCodeBERT via weighted logit-level fusion and achieves macro-F1 0.538 , outperforming comparable encoder-only systems. We additionally document a HuggingFace label inversion pitfall that suppressed our initial score by approximately 0.29 F1.