A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction
High-fidelity CFD for vehicle aerodynamic drag is bottlenecked not by solver wall time but by workflow friction—CAD cleanup, meshing retries, and queue contention. This paper proposes a contract-centric blueprint where self-evolving coding agents search over executable surrogate programs (not static models) to predict drag coefficient $C_d$ under industrial constraints. The system combines Famou-Agent-style evaluator feedback with population-based island evolution and hard evaluation contracts that enforce leakage prevention, deterministic replay, and resource budgets, aiming for a screen-and-escalate deployment where uncertain cases trigger automatic fallback to high-fidelity CFD.
The paper offers a systems-level contribution that treats surrogate discovery as an auditable engineering process rather than a model-tuning exercise. Its emphasis on hard contracts, multi-objective fitness balancing reliability against complexity, and explicit safety boundaries for out-of-distribution escalation reflects genuine industrial rigor. However, the evaluation relies on undisclosed datasets and anonymized LLM operators, limiting scientific reproducibility, while the deployment claims remain conceptual without demonstrated real-world validation metrics.
The contract-centric evaluation harness is a concrete contribution: candidates are rejected if they violate leakage, resource, or determinism gates regardless of accuracy. The ablation study (Section 6.3) provides convincing evidence that adaptive sampling and island migration are primary drivers of convergence quality, with the Full Method (Combined Score 0.8437) substantially outperforming variants without feedback (0.7782), without island model (0.7287), or without adaptive sampling (0.7117). The fitness function $F(c) = \omega_1 \cdot \text{Accuracy} + \omega_2 \cdot \text{Reliability} - \omega_3 \cdot \text{Complexity} - \text{Penalty}(\text{Contract\_Violations})$ explicitly encodes industrial priorities beyond leaderboard accuracy.
The paper lacks transparency on foundational experimental details. The eight evolutionary operators are anonymized (e.g., gemini-3.0-pro, gpt-5.2) without configuration specifics, and the dataset is vaguely described as heterogeneous public and industrial data without identifiers, sizes, or availability links. This opacity makes independent verification impossible. The Combined Score metric $S = \alpha \cdot \text{Acc}_{\text{sign}} + \beta \cdot \frac{1}{1+\text{RMSE}} + \gamma \cdot \frac{1}{1+\text{MAE}}$ weights sign accuracy heavily, but the weight selection rationale is not justified. Finally, Section 7 outlines a deployment blueprint and ROI formula, yet provides no evidence of actual production deployment, compression of design cycles, or realized cost savings.
The evaluation compares different LLM backends acting as evolutionary operators (Table 1) rather than comparing against strong static baselines such as Gaussian processes, FNOs, or human-engineered GNNs, making it unclear whether the evolutionary approach outperforms conventional surrogate methods. The claim that industrial reports require Spearman's $\rho > 0.9$ for trustworthy optimization is cited to general surrogate literature rather than specific validation studies. While the related work coverage is comprehensive across aerodynamic surrogates, AutoML, and evolutionary synthesis, the central dependency on Famou-Agent [17] shares authors with this work, creating potential circularity in the claimed improvements.
Despite extensive discussion of reproducibility contracts and deterministic replay, no code repository, dataset URL, or implementation details are provided. The paper emphasizes that a candidate is a versioned training program with deterministic preprocessing and provenance metadata, yet the specific genome representation, mutation operators, population sizes, island topology parameters, and LLM prompting strategies remain underspecified. Multi-seed robustness is claimed but the number of seeds and variance statistics are not reported. Without access to the evaluation harness or the data governance contracts described, independent reproduction of the 0.9335 Combined Score or the evolutionary trajectories shown in Figure 2 is infeasible.
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.
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