[2603.21698] A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction

Computer Science > Artificial Intelligence arXiv:2603.21698 (cs) [Submitted on 23 Mar 2026] Title:A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction Authors:Jinhui Ren, Huaiming Li, Yabin Liu, Tao Li, Zhaokun Liu, Yujia Liang, Zengle Ge, Chufan Wu, Xiaomin Yuan, Danyu Liu, Annan Li, Jianmin Wu View a PDF of the paper titled A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction, by Jinhui Ren and 11 other authors View PDF HTML (experimental) Abstract:High-fidelity vehicle drag evaluation is constrained less by solver runtime than by workflow friction: geometry cleanup, meshing retries, queue contention, and reproducibility failures across teams. We present a contract-centric blueprint for self-evolving coding agents that discover executable surrogate pipelines for predicting drag coefficient $C_d$ under industrial constraints. The method formulates surrogate discovery as constrained optimization over programs, not static model instances, and combines Famou-Agent-style evaluator feedback with population-based island evolution, structured mutations (data, model, loss, and split policies), and multi-objective selection balancing ranking quality, stability, and cost. A hard evaluation contract enforces leakage prevention, deterministic replay, multi-seed robustness, and resource budgets before any candidate is admitted. Across eight anonymized evolutionary operators, the best system reaches a Combined Score of 0.9335 ...