Regression-based prediction is widely applied to continuous outputs, such as emotion dimension estimation. However, traditional methods struggle to handle unclear annotation standards and ambiguous cases. To address this challenge, we propose a dual-layer agent-executor framework, where the agent is responsible for constructing and refining guidelines, while the executor applies these guidelines to annotate large-scale data. Notably, we introduce a novel refinement mechanism that can detect outlier instances and provide feedback to the agent for guideline revision, thereby achieving iterative improvement. We applied this method to the ROCLING 2025 shared task for predicting valence-arousal (VA) values in medical self-reflection texts. Compared to the unmodified version, the outlier-driven configuration effectively reduced MAE for both V/A, with A-MAE significantly decreased by 7.7%. The final valence-MAE was 0.51 and arousal-MAE was 0.87, ranking fourth.