@inproceedings{gao-etal-2026-uir,
    title = "uir-cis-7 at {S}em{E}val-2026 Task 7: Zero-Shot Chain-of-Thought Reasoning for Cross-Cultural Daily Knowledge",
    author = "Gao, Jianning  and
      Mao, Xianling  and
      Shi, Shumin  and
      Zhaxi, Duanzhi  and
      Sun, Yingbo  and
      Li, Xiandeng  and
      Li, Binyang",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.399/",
    pages = "3182--3187",
    ISBN = "979-8-89176-414-9",
    abstract = "SemEval-2026 Task 7 evaluates the ability of Large Language Models (LLMs) to reason about diverse daily knowledge across 30 geographic regions. In this paper, team uir-cis-7 approaches this challenge not merely as an accuracy optimization problem, but as a diagnostic probe to evaluate the representational limits of LLMs without fine-tuning. To address Western-centric bias and the ``overthinking penalty'' frequently observed in high-resource contexts, we introduce a Two-Tier Dynamic Routing framework. Based on cultural resource density, queries are routed either to a direct-answer pathway or a complex reasoning pathway. The complex pathway utilizes an Anti-Bias Persona-Conditioned Chain-of-Thought enhanced with Knowledge Anchoring and multi-path Self-Consistency voting to mitigate majority-culture heuristics. Evaluated using a strict macro-average metric, our system achieved an overall accuracy of 89.02{\%} on the official leaderboard. Our fine-grained evaluation and theoretical error analysis quantify the epistemological boundaries of prompt-based alignment, proving our dynamic strategy effectively rescues marginalized cultural knowledge while exposing persistent instances where safety-aligned models project Western progressive norms onto traditional contexts. Furthermore, cross-model validation on open-source architectures explicitly confirms our framework{'}s generalizability."
}