Multimodal Hybrid Retrieval with Guided Query Refinement (GQR)

📘 Tutorial | 🌐 Website | 📄 Paper

This repository contains the official code for the paper:

Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization, Uzan et al., ICLR 2026

GQR is a test-time optimization method for multimodal retrieval.
It improves a primary retriever retrieval by refining its query representation at test-time using similarity signals from a complementary retriever, enabling stronger hybrid retrieval without additional training.

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📂 Project Structure

The repository is organized into two main components:

1. GQR-Tutorial
   A lightweight walkthrough introducing the retrieval setting, baselines, and the GQR method.
   We recommend starting here to build intuition for the approach.

2. paper-repro
   Code, configurations, and scripts used to reproduce the experiments and results reported in the paper.

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📜 Citation

If you find this work useful, please cite:

@inproceedings{uzan2026guidedqueryrefinement,
  title     = {Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization},
  author    = {Uzan, Omri and Yehudai, Asaf and Pony, Roi and Shnarch, Eyal and Gera, Ariel},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2026},
  url       = {https://arxiv.org/abs/2510.05038}
}