LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
Quick Answer
This paper shows that A phase-aware LLM agent optimizes human-object interaction retrieval, outperforming Optuna TPE by 33.3% and VDTuner by 34.2% on the HICO-DET benchmark.
Quick Take
This method enhances throughput by 15.3x over UniIR and demonstrates strong transferability across vector database management systems.
Key Points
- The agent conditions proposals on full optimization history for better parameter navigation.
- Achieved a 15.3x throughput gain over UniIR in human-object interaction retrieval.
- Validation shows performance improves with parameter coupling: +33.3% on HICO-DET.
- Cross-system tests confirm top ranking across three datasets without modifications.
- Demonstrates effective optimization across different vector database management systems.
Paper Resources
Source Excerpt
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 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. …
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