Helios: A Foundational Language Model for Smart Energy Knowledge Reasoning and Application

• URL: http://arxiv.org/abs/2512.19299v1
• Date: Mon, 22 Dec 2025 11:43:35 GMT
• Title: Helios: A Foundational Language Model for Smart Energy Knowledge Reasoning and Application
• Authors: Haoyu Jiang, Fanjie Zeng, Boan Qu, Xiaojie Lin, Wei Zhong,
• Abstract summary: Helios is a large language model tailored to the smart energy domain.<n>Enersys is a multi-agent collaborative framework for end-to-end dataset construction.<n>We release EnerBench, a benchmark for evaluating LLMs in smart energy scenarios.
• Score: 7.294864378911716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the global drive toward carbon neutrality, deeply coordinated smart energy systems underpin industrial transformation. However, the interdisciplinary, fragmented, and fast-evolving expertise in this domain prevents general-purpose LLMs, which lack domain knowledge and physical-constraint awareness, from delivering precise engineering-aligned inference and generation. To address these challenges, we introduce Helios, a large language model tailored to the smart energy domain, together with a comprehensive suite of resources to advance LLM research in this field. Specifically, we develop Enersys, a multi-agent collaborative framework for end-to-end dataset construction, through which we produce: (1) a smart energy knowledge base, EnerBase, to enrich the model's foundational expertise; (2) an instruction fine-tuning dataset, EnerInstruct, to strengthen performance on domain-specific downstream tasks; and (3) an RLHF dataset, EnerReinforce, to align the model with human preferences and industry standards. Leveraging these resources, Helios undergoes large-scale pretraining, SFT, and RLHF. We also release EnerBench, a benchmark for evaluating LLMs in smart energy scenarios, and demonstrate that our approach significantly enhances domain knowledge mastery, task execution accuracy, and alignment with human preferences.

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