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：随着新型神经架构对电力的需求急剧增长，信息检索领域已将生态可持续性视为关键优先事项，这要求模型设计实现根本性范式转变。尽管当代神经排序器已取得前所未有的准确性，但其计算密集性伴随的巨大环境外部性在大规模部署中往往被忽视。我们提出GaiaFlow，一种通过操作化语义引导扩散调优来实现低碳搜索的创新框架。该方法融合了检索引导的朗之万动力学与硬件无关的性能建模策略，以优化搜索精度与环境保护之间的权衡。通过集成自适应早期退出协议与精度感知量化推理，所提架构在异构计算基础设施上显著降低运营碳足迹，同时保持稳健的检索质量。广泛实验评估表明，GaiaFlow在效果与能效之间实现了卓越平衡，为下一代神经搜索系统提供了可扩展且可持续的发展路径。