# Seeking cs.LG endorsement: on-device glucose trend forecasting and INT8 quantization > Source: > Published: 2026-09-20 02:38:04+00:00 Hi everyone, I’m an independent researcher preparing my first arXiv submission in **cs.LG**, and I’m looking for an eligible endorser willing to look at the work. **Paper:** GlucoEdge: An Engineering Study of On-Device Five-Class Glucose Trend Forecasting and INT8 Quantization It covers an end-to-end on-device ML pipeline for glucose trend forecasting from continuous glucose monitor data: five-class, 15-minute trend forecasting from CGM history; a compact 1D CNN with 2,909 parameters; PyTorch to LiteRT conversion; static INT8 post-training quantization; the effect of quantization on minority-class recall; calibration-data coverage and input-range clipping; cross-runtime numerical parity testing; and offline inference on a physical Android device. INT8 quantization cut the model artifact size by about 31%, but macro recall dropped from 0.5060 to 0.4135, with recall falling across all four directional classes. So the study looks at deployment size alongside what quantization does to minority-class behavior. I’ve tried to be explicit about the limitations: overlapping time-series windows, calibration-set selection, context-prefix leakage risk, single-device latency measurements, and the fact that this is a research prototype rather than a medical system. Code: [GitHub - mohamedmenasy/GlucoEdge · GitHub](https://github.com/mohamedmenasy/GlucoEdge) (Android implementation, training and conversion code, tests, model artifacts, and reproducibility materials) Requested category: **cs.LG** Endorsement code: **PC9MML** If you’re eligible to endorse in cs.LG and willing to look at the manuscript, I’d appreciate it. I’m also glad to get technical feedback even if you can’t endorse. Thanks,