GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908981720514560 |
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| author | Fu, Rong Tan, Jia Yee Meng, Chunlei Yin, Shuo Ma, Xiaowen Wu, Wangyu Qi, Muge Yao, Guangzhen Kang, Zhaolu Su, Zeli Fong, Simon |
| author_facet | Fu, Rong Tan, Jia Yee Meng, Chunlei Yin, Shuo Ma, Xiaowen Wu, Wangyu Qi, Muge Yao, Guangzhen Kang, Zhaolu Su, Zeli Fong, Simon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15423 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search Fu, Rong Tan, Jia Yee Meng, Chunlei Yin, Shuo Ma, Xiaowen Wu, Wangyu Qi, Muge Yao, Guangzhen Kang, Zhaolu Su, Zeli Fong, Simon Information Retrieval Machine Learning 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. |
| title | GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2602.15423 |