Title: Adaptive World Models for Data-Efficient Learning (AWML)
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Adaptive world models, Structured Latent Dynamics, Modular causal modeling, Neural operators, Counterfactual recombination

Abstract: We propose Adaptive World Models for Data-Efficient Learning (AWML), a framework that combines structured latent world models, counterfactual augmentation, and calibrated uncertainty filtering to improve performance in low-data regimes.

AWML learns modular latent dynamics guided by domain priors, generates counterfactuals through modular recombination, and accepts synthetic samples only when an uncertainty estimator passes a calibration check.

Our analysis shows that this design controls estimation error, constrains distributional deviation after augmentation, and clarifies the bias–variance trade-off in terms of estimation quality, acceptance thresholds, and data mass.

Instantiated with neural-operator backbones and modular causal blocks plus safeguards such as ensemble calibration and audit flags, AWML achieves both theoretical guarantees and practical robustness.

Empirically, it reduces error in synthetic autoregressive tasks and delivers substantial gains on Uganda LSMS 2019 survey data, boosting AUC from 0.8797 to 0.9402 with only 25 labels.

These results demonstrate that AWML provides a principled and effective approach for safe, data-efficient learning.

TL;DR: AWML is a framework that certifiably augments data via modular recombination and uncertainty filtering, yielding provable sample-efficiency gains—safely “making more data” when real data are scarce.