As the burgeoning power requirements of sophisticated neural architectures escalate, the information retrieval community has recognized ecological sustainability as a pivotal priority that necessitates a fundamental paradigm shift in model design. While contemporary neural rankers have attained unprecedented accuracy, the substantial environmental externalities associated with their computational intensity often remain overlooked in large-scale deployments. We present GaiaFlow, an innovative framework engineered to facilitate carbon-frugal search by operationalizing semantic-guided diffusion tuning. Our methodology orchestrates the convergence of retrieval-guided Langevin dynamics and a hardware-independent performance modeling strategy to optimize the trade-off between search precision and environmental preservation. By incorporating adaptive early exit protocols and precision-aware quantized inference, the proposed architecture significantly mitigates operational carbon footprints while maintaining robust retrieval quality across heterogeneous computing infrastructures. Extensive experimental evaluations demonstrate that GaiaFlow achieves a superior equilibrium between effectiveness and energy efficiency, offering a scalable and sustainable pathway for next-generation neural search systems.

翻译：随着复杂神经架构的功率需求日益增长，信息检索领域已认识到生态可持续性是一个关键优先事项，这需要在模型设计中实现根本性的范式转变。尽管当代神经排序模型已达到了前所未有的准确性，但其计算强度所带来的显著环境外部性在大规模部署中往往被忽视。我们提出了GaiaFlow，这是一个创新框架，旨在通过实现语义引导的扩散调优来促进碳节约搜索。我们的方法协调了检索引导的朗之万动力学与硬件无关的性能建模策略的融合，以优化搜索精度与环境保护之间的权衡。通过引入自适应早退协议和精度感知的量化推理，所提出的架构在异构计算基础设施上显著降低了运行碳足迹，同时保持了稳健的检索质量。广泛的实验评估表明，GaiaFlow在检索效果与能源效率之间实现了优越的平衡，为下一代神经搜索系统提供了一条可扩展且可持续的发展路径。