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Insights from Autoresearch for Solar Panel Segmentation

2026-10-07 17:45:31
Justinas Lekavicius, Kursat Komurcu, Valentas Gruzauskas, Linas Petkevicius
arXiv_CV
       
Abstract

This paper investigates AutoResearch, a protocol in which a coding language model edits a training program under a one-hour GPU budget and retains a change only if validation IoU improves. The protocol is applied to photovoltaic panel segmentation on a frozen real-image split, with DeepLabV3--ResNet-50 held fixed. Three campaigns of 24 experiments, using Gemma~4 12B, Qwen3-8B all improve their one-hour baselines, but retained modifications do not transfer across hardware. The Qwen3-8B configuration, trained on real images only, reaches a test IoU of 0.836 versus 0.833 for the reference GAN-augmented schedule. Research repository this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2610.10491

PDF

https://arxiv.org/pdf/2610.10491.pdf

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