Semantic textual similarity (STS) is central to retrieval, clustering, and semantic understanding, yet Arabic remains underserved by high-quality sentence-embedding models and evaluation resources. We introduce GATE, a compact family of Arabic sentence embedding models and a controlled study of Arabic-specific backbones, contrastive Matryoshka Representation Learning, dimensional truncation, and hybrid STS-NLI supervision. We evaluate the resulting models across Arabic semantic similarity, NLI, pair classification, sentence classification, clustering, retrieval, reranking, and dialectal semantic relatedness, against strong multilingual baselines including BGE-M3, Arctic-Embed-v2, and Qwen3-Embedding. Despite their 135M-parameter scale, GATE-family models are competitive with substantially larger multilingual encoders and achieve strong results on several native-Arabic semantic tasks. A three-annotator audit further finds high Arabic surface adequacy and 84.2% majority relation preservation for the translated supervision. The released checkpoints, data resources, and experimental artifacts support reproducible Arabic embedding research.