@inproceedings{liu-etal-2026-wildsci,
    title = "{W}ild{S}ci: Advancing Scientific Reasoning from In-the-Wild Literature",
    author = "Liu, Tengxiao  and
      Nathani, Deepak  and
      Li, Zekun  and
      Yang, Kevin  and
      Wang, William Yang",
    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.567/",
    pages = "11677--11695",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent progress in large language model (LLM) reasoning has focused on domains like mathematics and coding, where abundant high-quality data and objective evaluation metrics are readily available. In contrast, progress in scientific reasoning remains limited in domains such as medicine and materials science due to restricted dataset coverage and the inherent complexity of open-ended scientific questions. To address these challenges, we propose a general framework for sustainable scientific reasoning QA generation, and introduce WildSci, a new dataset of domain-specific science questions automatically synthesized from peer-reviewed literature, spanning 9 scientific disciplines and 26 subdomains. WildSci enables scalable training with well-defined reward signals in a multiple-choice format. We further apply reinforcement learning to finetune models on WildSci and analyze the resulting training dynamics, including domain-specific performance changes, response behaviors, and generalization trends. Experiments on a suite of scientific benchmarks demonstrate the effectiveness of our framework and dataset. We release WildSci to enable scalable and sustainable research in scientific reasoning."
}