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English(EN) LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval

LLM代理优化检索系统的ANN索引

研究人员开发了一种新颖的LLM引导代理,用于优化检索系统中的近似最近邻(ANN)索引参数。该代理通过将每个建议条件化于完整的优化历史,克服了传统超参数优化方法的局限性,有效地导航了复杂、耦合的参数空间。在HICO-DET人体-物体交互检索基准上进行测试,该代理展示了显著的性能提升,超越现有方法超过33%,并实现了15.3倍的吞吐量增长。 AI

影响 这种LLM引导的优化方法可以显著提高各种AI检索系统的效率和性能。

排序理由 这是一篇详细介绍AI系统参数优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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LLM代理优化检索系统的ANN索引

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这是一篇详细介绍AI系统参数优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Shahrzad Esmat, Chaunte W. Lacewell, Sameh Gobriel, Nilesh Jain, Ali Jannesari ·

    LLM 引导的人体-物体交互检索的 ANN 索引优化

    arXiv:2606.05489v1 Announce Type: new Abstract: Retrieval systems underpin modern AI applications -- spanning visual search, recommendation engines, and multi-modal question answering. Modern multi-stage retrieval systems require the joint optimization of highly coupled parameter…