# Uncertainty-Gated Generative Modeling

arXiv: https://arxiv.org/abs/2603.07753 · HTML: https://mlsanity.com/paper/2603.07753/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 8 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Advancing Multimodal Data Fusion in Pain Recognition: A Strategy Leveraging Statistical Correlation and Human-Centered Perspectives](https://mlsanity.com/paper/2404.00320/) (2024)
- [CauSkelNet: Causal Representation Learning for Human Behaviour Analysis](https://mlsanity.com/paper/2409.15564/) (2024)
- [Probabilistic Forecasting of Real-Time Electricity Market Signals via Interpretable Generative AI](https://mlsanity.com/paper/2403.05743/) (2024)

## Built on by (most cited first, 1 of 1)

- [Task-Aware Delegation Cues for LLM Agents](https://mlsanity.com/paper/2603.11011/) (2026)

## Abstract (arXiv)

Financial time-series forecasting is a high-stakes problem where regime shifts and shocks make point-accurate yet overconfident models dangerous. We propose Uncertainty-Gated Generative Modeling (UGGM), which treats uncertainty as an internal control signal that gates (i) representation via gated reparameterization, (ii) propagation via similarity and confidence routing, and (iii) generation via uncertainty-controlled predictive distributions, together with uncertainty-driven regularization and calibration to curb miscalibration. Instantiated on Weak Innovation AutoEncoder (WIAE-GPF), our UG-WIAE-GPF significantly improves risk-sensitive forecasting, delivering a 63.5% MSE reduction on NYISO (0.3508 rightarrow 0.1281), with improved robustness under shock intervals (mSE: 0.2739 rightarrow 0.1748).