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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 structured query operators with vector-retrieval primitives, allowing for complex logical compositions of text and image search. ProRetrieval was trained using Qwen3-4B with GRPO and DAPO, and it demonstrated superior performance on new benchmarks derived from Amazon products and Enron emails, outperforming models like GPT-5.5 and Claude Opus 4.7. AI

IMPACT This research advances hybrid search capabilities, potentially improving how LLMs interact with structured and unstructured data for complex queries.

RANK_REASON The cluster contains a research paper detailing a new system and benchmark for information retrieval. [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 for information retrieval. [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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