# Ideas & Hypotheses

## Transformer as backbone (Plan B)
- Hypothesis: self-attention may better capture long-range dependencies and reduce conflict between temporal consistency and distribution matching.
- Risk: higher compute cost, potentially more unstable training.
- Status: implemented as `backbone_type = "transformer"` in config.
- Experiment: compare GRU vs Transformer using `run_compare.py`.

## Residual standardization
- Hypothesis: standardizing residuals before diffusion reduces drift and improves KS.

## Two-stage training with curriculum
- Hypothesis: train diffusion on residuals only after temporal GRU converges to low error.

## Discrete calibration
- Hypothesis: post-hoc calibration on discrete marginals can reduce JSD without harming KS.

## Feature-type split modeling
- Hypothesis: separate generation per feature type (setpoints, controllers, actuators, quantized, derived, aux) yields better overall fidelity.