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

arXiv:2606.10084 · cs.LG, cs.AI · Submitted 2026-06-08 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-06-08

Updated: 2026-08-30

Comments: 3rd-place submission to the AI-DEEDS 2026 CTF-4-Science Chaotic Systems Challenge. Challenge Results: https://ai-deeds.github.io/2026/#results

Project page: https://ai-deeds.github.io/2026/#results

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 16× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Common Task Framework — 4× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Few-Shot Learning — 2× in this paper · explained in 4 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Assimilation — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
• The Seismic Wavefield Common Task Framework

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