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

• URL: http://arxiv.org/abs/2606.10084v1
• Date: Mon, 08 Jun 2026 19:08:22 GMT
• Title: Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark
• Abstract summary: This work evaluates chaotic-system prediction across twelve hidden scores and five scenario families.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs [48.245159309501666]
  Trajectory forecasting for autonomous driving has advanced rapidly, yet representative models often produce uninformative posteriors over forecast modes.<n>We argue that this K-means-like hard assignment (one-hot) is the source of uninformative mode probabilities.<n>We introduce two post-hoc treatments: (1) test-time posterior-weighted merging that aggregates nearby candidate trajectories; and (2) a one-step expectation-maximization (EM) update.
  arXiv  Detail & Related papers  (2026-06-24T22:26:43Z)
• Adaptive Reservoir Computing for Multi-Scenario Chaotic System Forecasting [0.0]
  We present an adaptive reservoir computing framework for the CTF-4-Science Lorenz benchmark.<n>We tailor the training and prediction procedure of Echo State Networks (ESNs) to the specific demands of each evaluation scenario.<n>The proposed framework achieves a score of 74.91 on the public benchmark leaderboard.
  arXiv  Detail & Related papers  (2026-05-27T08:27:02Z)
• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting [62.22445401483844]
  Existing methods are severely constrained by the bottleneck of compounding errors.<n>We propose a universal framework called -Cast-Corrector (Plug-and-Play Corrector)<n>Our method significantly enhances the long-term stability of coupled forecasting systems.
  arXiv  Detail & Related papers  (2026-05-09T13:12:33Z)
• A Predictive View on Streaming Hidden Markov Models [0.8122270502556375]
  We develop a predictive-first optimisation framework for streaming hidden Markov models.<n>Our objective is to sequentially identify latent regimes while maintaining accurate step-ahead predictive distributions.
  arXiv  Detail & Related papers  (2026-04-10T10:51:50Z)
• Noise Titration: Exact Distributional Benchmarking for Probabilistic Time Series Forecasting [1.5939955861266883]
  We propose a paradigm shift toward interventionist, exact-statistical benchmarking.<n>We transform forecasting from a black-box sequence matching game into an exact distributional inference task.<n>We find that state-of-the-art zero-shot models behave consistently with the context-parroting mechanism.
  arXiv  Detail & Related papers  (2026-03-23T17:14:11Z)
• Controllable Probabilistic Forecasting with Stochastic Decomposition Layers [1.3995263206621]
  We introduce Decomposition Layers (SDL) for converting deterministic machine learning weather models into ensemble systems.<n>SDL applies learned perturbations at three decoder scales through latent-driven modulation, per-pixel noise, and channel scaling.<n>When applied to WXFormer via transfer learning, SDL requires less than 2% of the computational cost needed to train the baseline model.
  arXiv  Detail & Related papers  (2025-12-21T17:10:00Z)
• Inference-Time Scaling of Diffusion Language Models with Particle Gibbs Sampling [70.8832906871441]
  We study how to steer generation toward desired rewards without retraining the models.<n>Prior methods typically resample or filter within a single denoising trajectory, optimizing rewards step-by-step without trajectory-level refinement.<n>We introduce particle Gibbs sampling for diffusion language models (PG-DLM), a novel inference-time algorithm enabling trajectory-level refinement while preserving generation perplexity.
  arXiv  Detail & Related papers  (2025-07-11T08:00:47Z)
• Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing [58.52119063742121]
  Retraining a model using its own predictions together with the original, potentially noisy labels is a well-known strategy for improving the model performance.<n>This paper addresses the question of how to optimally combine the model's predictions and the provided labels.<n>Our main contribution is the derivation of the Bayes optimal aggregator function to combine the current model's predictions and the given labels.
  arXiv  Detail & Related papers  (2025-05-21T07:16:44Z)
• Attention-Based Ensemble Pooling for Time Series Forecasting [55.2480439325792]
  We propose a method for pooling that performs a weighted average over candidate model forecasts. We test this method on two time-series forecasting problems: multi-step forecasting of the dynamics of the non-stationary Lorenz 63 equation, and one-step forecasting of the weekly incident deaths due to COVID-19.
  arXiv  Detail & Related papers  (2023-10-24T22:59:56Z)
• Debiased Fine-Tuning for Vision-language Models by Prompt Regularization [56.48290708901531]
  We present a new paradigm for fine-tuning large-scale vision pre-trained models on downstream task, dubbed Prompt Regularization (ProReg)<n>ProReg uses the prediction by prompting the pretrained model to regularize the fine-tuning.<n>We show the consistently strong performance of ProReg compared with conventional fine-tuning, zero-shot prompt, prompt tuning, and other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-01-29T11:53:55Z)
• Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning [24.248673415586413]
  We introduce a Markovian probability model to characterize the intrinsic temporal structure of the model aggregation series. We develop a message passing algorithm, termed temporal-structure-assisted gradient aggregation (TSA-GA), to fulfil this estimation task. We show that the proposed TSAGA algorithm significantly outperforms the state-of-the-art, and is able to achieve comparable learning performance.
  arXiv  Detail & Related papers  (2021-03-03T09:13:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.