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
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 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
@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.