"""GEMEO-CDF: Causal Diffusion Forcing for clinical trajectories.

Three "first in medicine" hooks:
  1. DIFFUSION FORCING (Chen MIT NeurIPS 2024 → Dreamer 4 Hafner 2025 backbone)
     — independent per-token noise levels unify AR + diffusion + counterfactual
       in ONE loss. Zero clinical port as of May 2026.

  2. LATENT ACTION MODEL (Genie / DeepMind 2024)
     — VQ-VAE codebook over (state_t, state_{t+1}) deltas discovers a
       treatment vocabulary without RxNorm/ATC labels. Solves the APAC
       miscoding / sparsity / off-label labelling pain in DATASUS.

  3. PROCESS REWARD VERIFIER (o3 / MAI-DxO 2025 pattern)
     — small PRM scores top-K rollouts at inference, returns top-1 +
       uncertainty band. Deliberative trajectory generation, novel in EHR.

Modules:
  diffusion_forcing.py  — core architecture (per-token noise + block-causal)
  lam.py                — Latent Action Model (VQ-VAE codebook)
  train_cdf.py          — training loop with diffusion forcing objective
  sample.py             — sampling: AR mode / denoise mode / counterfactual
  distill.py            — Shortcut Forcing distillation (Dreamer 4)
  prm.py                — Process Reward Verifier
"""
from .diffusion_forcing import CDFTransformer, CDFConfig
from .lam import LatentActionVQVAE, LAMConfig
from .train_cdf import train_cdf

__all__ = [
    "CDFTransformer", "CDFConfig",
    "LatentActionVQVAE", "LAMConfig",
    "train_cdf",
]