🤖 AI Summary
This work addresses the challenge of predicting chaotic dynamics across multiple scenarios in the CTF-4-Science Lorenz benchmark—including clean prediction, noisy reconstruction, prediction from noisy inputs, few-shot learning, and parameter generalization—by proposing a scenario-specific modeling strategy that abandons the pursuit of a single universal model. The approach integrates specialized modules tailored to each task: trajectory smoothing for denoising, NG-RC/NVAR-based attractor prediction, sensitive-prefix-guided Lorenz dynamical correction, and parametric prefix interpolation. Each component is optimized according to the characteristics of its target scenario. Evaluated on the benchmark, this method achieves a public score of 79.63, substantially outperforming generic models and demonstrating the efficacy and superiority of a scenario-aware, specialized modeling paradigm for chaotic system prediction.
📝 Abstract
This work presents a divide-and-conquer modeling strategy for the CTF-4-Science Lorenz benchmark, which evaluates 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.