@inproceedings{kim-etal-2026-program,
    title = "{PROGRAM}: Programmatic Retrieval Optimization with Generative Reasoning and Augmented Multi-queries",
    author = "Kim, Gun Il  and
      Shin, Jungkyu  and
      Kim, Jong Wook  and
      Jang, Beakcheol",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1090/",
    pages = "21685--21699",
    ISBN = "979-8-89176-395-1",
    abstract = "Current retrieval-augmented generation (RAG) methods struggle with complex multi-hop reasoning, relying on unstructured semantic matching that lacks the logical structure needed to systematically guide retrieval. We introduce Programmatic Retrieval Optimization with Generative Reasoning and Augmented Multi-queries (PROGRAM), a novel framework that elevates retrieval to structured, program-guided reasoning. PROGRAM treats retrieval as execution of specific program types, such as logical, temporal, causal, and so forth, through three stages of `Program-Type Selection' with dual-metric optimization, `Iterative Active Program Pruning' with evidence accumulation, and `Final Answer Generation' with reranking. Evaluated on five benchmarks including HotPotQA, 2WikiMultihopQA, ARC-Challenge, MMLU-Pro, and MedQA with various LLMs, PROGRAM achieves state-of-the-art performance with up to 24{\%} relative improvement on HotPotQA and 13.2{\%} on MedQA over strong baselines including FLARE, ProbTree and Self-RAG."
}