@inproceedings{xiao-etal-2026-lunar,
    title = "Lunar-Bench: Towards Evaluating Task-Oriented Reasoning of {LLM}s in Lunar Exploration Scenarios",
    author = "Xiao, Xin-Yu  and
      Tian, Ye  and
      Yin, Erwei  and
      He, Zhixian  and
      Wang, Shiqi  and
      Liu, Yalei  and
      Xia, Qianchen",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.83/",
    pages = "1668--1705",
    ISBN = "979-8-89176-395-1",
    abstract = "The increasing complexity of lunar exploration calls for intelligent systems capable of supporting autonomous operations and scientific decision-making under uncertain and resource-limited conditions. Advances in large language models (LLMs) create new opportunities for mission planning, but their reliability in dynamic, safety-critical environments remains insufficiently evaluated. Existing benchmarks focus on static, context-independent reasoning tasks and fail to capture the constraints and dependencies of lunar missions. To address this gap, we introduce Lunar-Bench, a benchmark designed to assess the task-oriented reasoning and decision-making performance of LLMs through 3,000 tasks derived from mission procedures and documentation. We further propose the Environmental Scenario Indicators, a process-based framework that evaluates safety, efficiency, integrity, and alignment beyond conventional accuracy. Experiments on 36 representative models show that the best achieves 47.8{\%} accuracy compared with 65.1{\%} for human experts. Lunar-Bench and ESI together provide a principled foundation for developing reliable systems for future missions."
}