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908ea05 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | """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",
]
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