EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention

Code Language Models (CodeLLMs) learn token importance from data correlations, whereas human developers attend selectively to semantically salient code. We present EYEMULA-TOR, a model-agnostic method that injects human visual-attention priors into CodeLLM finetuning without architectural changes. EYEMU-LATOR distills eye-tracking data into semantic salience and gaze-transition priors, then uses them to reweight token-level training losses. Across six backbones, two data regimes, and three CodeXGLUE tasks, the reported configurations yield positive matched-metric deltas in all 36 model-task-setting cells. Effects are largest for structure-preserving completion and translation, while summarization shows smaller but positive METEOR deltas. Session-mode and component-ablation analyses further show that reading, writing, semantic, and transitionderived priors provide complementary signal. Human-attention artifacts are available at http s://zenodo.org/records/17205682.