LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
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 parameters, yet traditional hyperparameter optimization (HPO) methods -- including Tree-structured Parzen Estimators (TPE) and Gaussian Process Bayesian Optimization -- rely on an independence assumption that fundamentally prevents them from navigating these coupled configuration spaces. We address this limitation with a phase-aware large language model (LLM) agent that conditions each proposal on its full optimization history, navigating the coupled parameter space acr...
| Authors | Shahrzad Esmat, Chaunte W. Lacewell, Sameh Gobriel et al. |
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| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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