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arXiv · 2610.10491

Insights from Autoresearch for Solar Panel Segmentation

Justinas Lekavicius·Kursat Komurcu·Valentas Gruzauskas·Linas Petkevicius
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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 https://github.com/VU-AIML/automl4eo-autoresearch-segmentation.

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Justinas Lekavicius, Kursat Komurcu, Valentas Gruzauskas, Linas Petkevicius. 2026-10-07. Insights from Autoresearch for Solar Panel Segmentation. https://arxiv.org/abs/2610.10491

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