mlsanityData to 14 Oct 2026

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

arXiv 2512.19299 ↗First posted 22 Dec 2025cs.AIcited by 1

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 13 of its 59 references that are in the map. First posted on arXiv 22 Dec 2025.

Builds on

13 of 59 references.

References · 13

  1. 2025Qwen2.5-VL Technical Reportcited by 5,587
  2. 2024Qwen2.5 Technical Reportcited by 4,942
  3. 2023Qwen Technical Reportcited by 3,344
  4. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  5. 2024OpenAI o1 System Cardcited by 1,244
  6. 2024DeepSeek-V3 Technical Reportcited by 811
  7. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 769
  8. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  9. 2023SemDeDup: Data-efficient learning at web-scale through semantic deduplicationcited by 303
  10. 2023Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Gamecited by 152
  11. 2025HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptationcited by 114
  12. 2024ChemLLM: A Chemical Large Language Modelcited by 106
1 more
  1. 2023Chainpoll: A high efficacy method for LLM hallucination detectioncited by 39

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branchescited by 0

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  1. 2025From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins
  2. 2025RE-LLM: Integrating Large Language Models into Renewable Energy Systems
  3. 2025Bridging AI and Carbon Capture: A Dataset for LLMs in Ionic Liquids and CBE Research
  4. 2023A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability
  5. 2025Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings
  6. 2024Domain-specific ReAct for physics-integrated iterative modeling: A case study of LLM agents for gas path analysis of gas turbines
  7. 2025Knowledge Distillation from Large Language Models for Household Energy Modeling
  8. 2023Opportunities and Challenges of Applying Large Language Models in Building Energy Efficiency and Decarbonization Studies: An Exploratory Overview