Open-ended text generation faces a critical challenge: balancing coherence with diversity in LLM outputs. While contrastive search-based decoding strategies have emerged to address this trade-off, their practical utility is often limited by hyperparameter dependence and high computational costs. We introduce GUARD, a hyperparameter-free decoding method that effectively balances these competing objectives through a novel "Glocal" uncertainty-driven framework. This approach combines Global Entropy estimates with local entropy deviations to integrate both long-term and short-term uncertainty signals. We show that our proposed Global Entropy formulation mitigates abrupt variations in uncertainty, such as sudden overconfidence or high entropy spikes, with theoretical guarantees of unbiasedness and consistency. To reduce computational overhead, we incorporate a straightforward but effective token count-based penalty. Experimental results demonstrate that GUARD achieves significant improvements in text diversity and coherence. Furthermore, both human and LLM evaluations confirm GUARD's strong performance across multiple dimensions: semantic coherence, fluency, factuality, informativeness, interestingness, and story development. Our code, datasets, and results are publicly available https://anonymous.4open.science/r/GUARD/README.md.