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

arXiv: https://arxiv.org/abs/2606.10084 · HTML: https://mlsanity.com/paper/2606.10084/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [The Seismic Wavefield Common Task Framework](https://mlsanity.com/paper/2512.19927/) (2025)
- [CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models](https://mlsanity.com/paper/2605.15549/) (2026)

## Abstract (arXiv)

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.