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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.21698 |
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| _version_ | 1866915883062919168 |
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| author | Ren, Jinhui Li, Huaiming Liu, Yabin Li, Tao Liu, Zhaokun Liang, Yujia Ge, Zengle Wu, Chufan Yuan, Xiaomin Liu, Danyu Li, Annan Wu, Jianmin |
| author_facet | Ren, Jinhui Li, Huaiming Liu, Yabin Li, Tao Liu, Zhaokun Liang, Yujia Ge, Zengle Wu, Chufan Yuan, Xiaomin Liu, Danyu Li, Annan Wu, Jianmin |
| contents | 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 with sign-accuracy 0.9180, while trajectory and ablation analyses show that adaptive sampling and island migration are primary drivers of convergence quality. The deployment model is explicitly ``screen-and-escalate'': surrogates provide high-throughput ranking for design exploration, but low-confidence or out-of-distribution cases are automatically escalated to high-fidelity CFD. The resulting contribution is an auditable, reusable workflow for accelerating aerodynamic design iteration while preserving decision-grade reliability, governance traceability, and safety boundaries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21698 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction Ren, Jinhui Li, Huaiming Liu, Yabin Li, Tao Liu, Zhaokun Liang, Yujia Ge, Zengle Wu, Chufan Yuan, Xiaomin Liu, Danyu Li, Annan Wu, Jianmin Artificial Intelligence 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 with sign-accuracy 0.9180, while trajectory and ablation analyses show that adaptive sampling and island migration are primary drivers of convergence quality. The deployment model is explicitly ``screen-and-escalate'': surrogates provide high-throughput ranking for design exploration, but low-confidence or out-of-distribution cases are automatically escalated to high-fidelity CFD. The resulting contribution is an auditable, reusable workflow for accelerating aerodynamic design iteration while preserving decision-grade reliability, governance traceability, and safety boundaries. |
| title | A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.21698 |