针对混沌系统预测挑战，分治策略提升多场景下预测精度。
Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark
- 按任务类型匹配不同模型，分治处理各类预测场景。
- 在噪声数据和参数变化场景中表现优异，最终得分79.63。
- 适合需要高鲁棒性与多场景适配的复杂系统建模研究者。
本文记录了为2026年AI-DEEDS举办的CTF-4-Science Lorenz混沌系统挑战所开发的分治建模策略。该挑战基于CTF-4-Science Lorenz基准，评估十二个隐藏评分和五类场景下的混沌系统预测能力：清洁预报、噪声重构、噪声输入预报、小样本学习与参数泛化。不采用单一模型覆盖所有情况，而是将每个预测模块与任务组的评估行为相匹配。主要贡献包括：基于平滑的重建方法用于噪声全轨迹去噪；针对噪声长时吸引子预报优化的NG-RC/NVAR模型；仅作用于敏感短时前缀的洛伦兹转移修正；以及用于插值任务的参数前缀融合。最终系统在公开评测中取得79.63分，表明在混合混沌预测基准上，有限的场景特异性更新优于泛化模型替换。
原文摘要 · Abstract (English)
This submission documents the divide-and-conquer modeling strategy developed for the CTF-4-Science Lorenz Chaotic Systems Challenge at AI-DEEDS 2026. The challenge uses the CTF-4-Science Lorenz benchmark to evaluate chaotic-system prediction across twelve hidden scores and five scenario families: clean forecasting, noisy reconstruction, noisy-input forecasting, few-shot learning, and parametric generalization. Rather than forcing one model class to handle all regimes, the final system matched each prediction block to the evaluation behavior of its task group. The main contributions are: smoothing-based reconstruction for noisy full-trajectory denoising; NG-RC/NVAR models tuned for noisy long-time attractor forecasting; a fitted Lorenz transition correction restricted to the sensitive clean short-time prefix; and a parametric prefix blend for the interpolation task. The resulting system with final public score of 79.63 shows that bounded, scenario-specific updates can outperform broad model replacement on mixed chaotic forecasting benchmarks.
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