GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search

• URL: http://arxiv.org/abs/2602.15423v1
• Date: Tue, 17 Feb 2026 08:35:11 GMT
• Title: GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search
• Authors: Rong Fu, Wenxin Zhang, Jia Yee Tan, Chunlei Meng, Shuo Yin, Xiaowen Ma, Wangyu Wu, Muge Qi, Guangzhen Yao, Zhaolu Kang, Zeli Su, Simon Fong,
• Abstract summary: We present GaiaFlow, an innovative framework engineered to facilitate carbon-frugal search by operationalizing semantic-guided diffusion tuning.<n>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.
• Score: 11.74886368366703
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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