Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark

Abstract: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.

3rd-place submission to the AI-DEEDS 2026 CTF-4-Science Chaotic Systems Challenge. Challenge Results: this https URL

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2606.10084 [cs.LG]

(or arXiv:2606.10084v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.10084 Focus to learn more arXiv-issued DOI via DataCite

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