---
license: mit
tags:
  - network-monitoring
  - self-supervised-learning
  - jepa
  - representation-learning
  - networking
---

# LPSE — Low-latency Semantic State Estimator

Model weights for **LPSE**, a low-latency semantic state estimator for dynamic network
monitoring and orchestration, built on I-JEPA-style latent predictive learning over
streaming telemetry.

Paper: *A Low-Latency Semantic State Estimator using Latent Predictive Learning for
Dynamic Network Monitoring and Orchestration* (IEEE GLOBECOM 2026, to appear).

On a multi-node Kubernetes testbed, the framework reaches 82.42% semantic prediction
accuracy under changing node configurations, with ~41x lower mean inference latency and
a 15x smaller memory footprint than a deployable 4B LLM endpoint.

## Files

| File | Contents |
|---|---|
| `lpsehybrid.pt` | Full LPSE training checkpoint for the canonical merged-dataset run (model, EMA encoder, JEPA predictor, optimizer state) |
| `baseline_mlp_v2.pt` | MLP baseline checkpoint |
| `baseline_xgb_v2.json` | XGBoost baseline model |
| `baseline_xgb_v2_encoder.pt` | Frozen encoder used to build XGBoost features |
| `baseline_xgb_v2_norm.pkl` | Feature normalizer for the XGBoost baseline |

## Usage

```bash
git clone https://github.com/hari5g900/lpse.git
cd lpse
pip install -e .

CHECKPOINT_PATH=/path/to/lpsehybrid.pt python -m lpse.apps.infer_lpse
```

See the [GitHub repository](https://github.com/hari5g900/lpse) for full training,
evaluation, and environment setup instructions (configuration is env-var driven; a GPU
is required for the LPSE and LSTM/Transformer baselines).

## Citation

```bibtex
@inproceedings{lpse2026,
  title     = {A Low-Latency Semantic State Estimator using Latent Predictive
               Learning for Dynamic Network Monitoring and Orchestration},
  booktitle = {IEEE GLOBECOM},
  year      = {2026},
  note      = {To appear}
}
```

## License

MIT — see [LICENSE](https://github.com/hari5g900/lpse/blob/main/LICENSE).