@inproceedings{wang-etal-2026-scalar,
    title = "{SCALAR}: Scientific Citation-based Live Assessment of Long-context Academic Reasoning",
    author = "Wang, Renxi  and
      Mu, Honglin  and
      Ma, Liqun  and
      Lin, Lizhi  and
      Feng, Yunlong  and
      Baldwin, Timothy  and
      Han, Xudong  and
      Li, Haonan",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.366/",
    pages = "7817--7830",
    ISBN = "979-8-89176-380-7",
    abstract = "Long-context understanding has emerged as a critical capability for large language models (LLMs). However, evaluating this ability remains challenging. We present SCALAR, a benchmark designed to assess citation-grounded long-context reasoning in academic writing. SCALAR leverages academic papers and their citation structure to automatically generate high-quality ground-truth labels without human annotation. It features controllable difficulty levels and a dynamic updating mechanism that mitigates data contamination. The benchmark includes two tasks: a multiple-choice QA format and a cloze-style citation prediction. We evaluate a range of state-of-the-art LLMs and find that the multiple-choice task effectively distinguishes model capabilities{---}while human experts achieve over 90{\%} accuracy, most models struggle. The cloze-style task is even more challenging, with no model exceeding 40{\%} accuracy. SCALAR provides a domain-grounded, continuously updating framework for tracking progress in citation-based long-context understanding. Code and data will be publicly released."
}