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.