---
license: cc-by-nc-4.0
library_name: pytorch
tags:
  - time-series
  - multi-task-learning
  - call-center
  - lstm
  - research-only
pipeline_tag: tabular-regression
---

# 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).

Given an intake record, an advisor profile, and the advisor's recent call history, forecasts three call outcomes: handle time, first-contact resolution (FCR) probability, and CSAT. Designed to be composed downstream of the [intake model](https://huggingface.co/datamatters24/attuned-resonance-intake) and upstream of the [PPO router](https://huggingface.co/datamatters24/attuned-resonance-router).

**Research/educational use only.** See disclaimer below.

> **Compatibility note (2026-04):** these weights were trained against the **previous** 10-class intake schema. The intake model has since been retrained on a clean 8-class schema, so the `intent_idx / len(INTENT_LABELS)` channel of the input now lives on a slightly different scale at inference time. End-to-end cascade output is best read as illustrative until the predictor is retrained against the new intake.

## Architecture

```
intake_features  (7-dim)    ┐
advisor_features (14-dim)   ├──▶  concat ──▶  MLP head ─┬─▶ handle_time  (regression, 0–1, rescaled ×1800s)
advisor_history  (30 × 6)   ┘      │                    ├─▶ fcr          (binary, sigmoid)
                                   │                    └─▶ csat         (regression, 0–1, rescaled to 1–5)
                           LSTM encoder
                           (30-step history → hidden state)
```

Small model (~120k parameters). The LSTM encodes the advisor's last 30 calls as a temporal sequence; its final hidden state is concatenated with the advisor profile and intake features, then fed to per-task heads.

## Training

- **Data**: synthetic data pipeline — ~60k calls × 500 advisors × 30 days (default generator config)
- **Intake features**: derived from intake labels (intent-index, sentiment, urgency, complexity, jung-index, campbell-index, archetype-confidence), each normalized to [0, 1]
- **Optimizer**: Adam, batch size 256
- **Epochs**: ~6 (plateaued; early-stopped via best-val checkpoint)
- **Hardware**: CPU (RunPod RTX 5090 had a cuDNN LSTM compatibility issue on Blackwell — known issue, fix pending)
- **Tracked**: MLflow experiment `prod-predictor`

### Metrics (best checkpoint)
- **val_loss**: 0.6367 (sum of handle-time MSE + FCR BCE + CSAT MSE)
- Plateau reached by epoch 3–6

## Intended Use

- Research on multi-task tabular+sequence regression
- Educational demonstrations of feature encoding discipline (matching training and inference encodings)
- 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 7-dim intake vector uses a specific normalization scheme (see `models/predictor/dataset.py::_encode_intake`). Mismatched encoding produces silently wrong predictions.
- **Router integration pending.** The PPO router (v2.1 roadmap) will consume this predictor's outputs as part of its reward signal.

## How to Load

```python
import numpy as np
from models.predictor.inference import OutcomeEstimator
from pathlib import Path

estimator = OutcomeEstimator(
    model_path=Path("trained_models/predictor/model.pt"),
    device="cpu",
)

intake_features  = np.array([...], dtype=np.float32)  # shape (7,)
advisor_features = np.array([...], dtype=np.float32)  # shape (14,)
advisor_history  = np.array([...], dtype=np.float32)  # shape (30, 6)

outcome = estimator.predict(intake_features, advisor_features, advisor_history)
# {'handle_time_seconds': 1550.0, 'fcr_probability': 0.41, 'csat_predicted': 3.4}
```

Full pipeline code at [tedrubin80/CEPM](https://github.com/tedrubin80/CEPM).

## License

CC-BY-NC-4.0. Non-commercial use only. Attribution required.

## 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}
}
```