GaiaFlow：基于语义的扩散调优用于碳节约搜索
GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search
AI总结：
GaiaFlow通过结合语义引导的扩散调优与硬件无关的性能建模策略，在保证检索精度的同时降低环境影响，实现了高效且可持续的神经搜索系统。
摘要：
随着复杂神经架构的能耗需求增长，信息检索领域认识到生态可持续性成为关键优先事项，需要在模型设计上进行根本性变革。尽管当前神经排序器已取得前所未有的准确性，但其计算强度带来的显著环境影响常被忽视。我们提出了GaiaFlow，一种创新框架，旨在通过操作化语义引导的扩散调优来促进碳节约搜索。我们的方法整合了检索引导的兰格-德动态和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。通过引入自适应早退协议和精度感知的量化推理，所提出架构在异构计算基础设施上显著降低了操作碳足迹，同时保持了稳健的检索质量。广泛实验评估表明，GaiaFlow在效果与能效之间实现了更优的平衡，为下一代神经搜索系统提供了可扩展且可持续的路径。
英文摘要：
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.
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