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ProRetrieval system synthesizes hybrid search programs, outperforming GPT-5.5 and Claude Opus 4.7

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ProRetrieval, a novel system that synthesizes executable programs to orchestrate hybrid search queries. This system combines SQL operators for structured data with vector retrieval for text and images, using SQL as the logical algebra to fuse results. In evaluations, a ProRetrieval model using Qwen3-4B with GRPO and DAPO outperformed GPT-5.5 and Claude Opus 4.7 on new benchmarks derived from Amazon products and Enron emails. AI

IMPACT This research demonstrates a novel approach to hybrid search orchestration, potentially improving information retrieval systems by effectively combining structured and unstructured data.

RANK_REASON The cluster contains a research paper detailing a new system and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ProRetrieval system synthesizes hybrid search programs, outperforming GPT-5.5 and Claude Opus 4.7

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The cluster contains a research paper detailing a new system and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Nan Du ·

    ProRetrieval: Learning to Orchestrate Hybrid Search via Executable Program Synthesis

    Real-world retrieval often composes structured constraints with semantic intents over text and images through arbitrary Boolean logic. Existing hybrid pipelines such as reciprocal rank fusion or self-querying retrievers admit only a fixed form of composition, while recent reinfor…

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