Attuned Resonance Outcome Predictor — Multi-Head LSTM

The GitHub repo slug is still "CEPM" during the gradual rename; the Hugging Face slugs were migrated to attuned-resonance-* on 2026-05-09 (HF preserves the old cepm-* URLs as redirects).

Architecture

intake_features  (17-dim)   ┐
  └─ 7 scalar features       │
  └─ 10-dim tone_probs       ├──▶  concat ──▶  MLP fusion (64-dim) ─┬─▶ handle_time  (regression, ×1800s)
advisor_features (14-dim)   │                                        ├─▶ fcr          (sigmoid)
advisor_history  (30 × 6)   ┘                                        └─▶ csat         (regression, 1–5)
                                   LSTM encoder
                                   (30-step history → 64-dim hidden)

Training

• Data: synthetic data pipeline — 56,693 calls × 500 advisors × 30 days
• Intake features: 17-dim — 7 normalized scalars + 10-dim Dirichlet-sampled voice-tone probability vector (cascade integration, 2026-05)
• Optimizer: Adam, batch size 256
• Epochs: 13 (early stop, patience=5)
• Hardware: CPU
• Tracked: MLflow experiment prod-predictor

Metrics (best checkpoint)

• best_val_loss: 0.6353 (sum of handle-time MSE + FCR BCE + CSAT MSE)
• train_loss: 0.6333 — minimal train/val gap, no overfitting

Cascade Position

audio ──▶ VoiceToneClassifier ──▶ tone_probs (10-dim)
                                        │
transcript ──▶ IntakePredictor ──▶ intent, sentiment, urgency, ...
                                        │
                              concat (17-dim) ──▶ OutcomeEstimator ──▶ handle_time, FCR, CSAT
                                                         │
                                              advisor profile + history

Intended Use

• Research on multi-task tabular+sequence regression
• Educational demonstrations of cascade feature composition (audio → NLP → outcome)
• Benchmarking against the synthetic-data environment in this repo

Out of Scope / Not Intended

• Any production or commercial use. Not validated for operational deployment.
• Real-world outcome prediction. Trained on synthetic data with known distributional simplifications.
• High-stakes decision support.

Limitations

• Synthetic data only — no real outcome distributions. The model learns the generator's biases.
• Short history dependence — only the last 30 advisor calls are visible.
• Feature encoding must match training. The 17-dim intake vector uses a specific normalization scheme (see models/predictor/dataset.py::_encode_intake). Mismatched encoding produces silently wrong predictions.
• Tone probs are synthetic at training time. The Dirichlet draw approximates real classifier output but doesn't capture true audio-derived distributions. Will improve once real audio is available end-to-end.
• Router integration pending. The PPO router will consume this predictor's outputs as part of its reward signal.

How to Load

License

Citation

@software{attuned_resonance_predictor_2026,
  author = {Rubin, Ted},
  title = {Attuned Resonance Outcome Predictor: Multi-Head LSTM for Call Outcome Forecasting},
  year = {2026},
  url = {https://github.com/tedrubin80/CEPM}
}