| """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", |
| ] |
|
|
