--- title: "Verbal-R3 — paper by Park et al. (2026) — summary and ideas" description: "The authors propose Verbal Annotations: short analytic texts explaining how a document relates to the query, which parts… 58 references." authors: ["Sangkwon Park", "Donghun Kang", "Jisoo Mok", "Sungroh Yoon"] year: 2026 venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)" source: "https://aclanthology.org/2026.acl-long.1712/" doi: "10.18653/v1/2026.acl-long.1712" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/park-2026-verbal-r3-verbal-reranker-as/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/park-2026-verbal-r3-verbal-reranker-as/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/55/) / Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning # Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning **Authors:** Sangkwon Park, Donghun Kang, Jisoo Mok and [Sungroh Yoon](https://md.research.elamur.ai/free/en/authors/sungroh-yoon/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1712/) · [PDF](https://aclanthology.org/2026.acl-long.1712.pdf) · [DOI 10.18653/v1/2026.acl-long.1712](https://doi.org/10.18653/v1/2026.acl-long.1712) · [OpenAlex](https://openalex.org/W7166845496) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary Standard retrieval-augmented generation pastes raw retrieved passages into an LLM's prompt. Those passages often contain distracting material and differ in style from the model's training data, so the evidence is not used well. The authors propose Verbal Annotations: short analytic texts explaining how a document relates to the query, which parts matter and which can be ignored. In a pilot study with Search-R1, GPT-OSS-120B annotations raised answer accuracy, while plain paraphrasing did not. Building on this, they present Verbal-R3, an agentic RAG system with two parts: - a Generator that alternates searching and reasoning; - a Verbal Reranker that scores retrieved documents, reorders them and rewrites each as an annotation. The reranker is distilled from GPT-OSS-120B into 1.5B/3B models via supervised fine-tuning. The Generator is then aligned to it with GRPO. At inference, relevance-guided test-time scaling spends compute only on promising queries and trajectories. The system reports state-of-the-art results on multi-hop QA benchmarks. This suggests that interpreting evidence, not just ranking it, is a key link between retrieval and reasoning. ## Key points - **Pilot study:** with Search-R1 3B, average QA accuracy across seven benchmarks (including 2Wiki) was: - 38.75 with raw passages; - 37.78 with paraphrased passages; - 41.92 with Verbal Annotations. - **Training pipeline:** query–document–annotation triplets from GPT-OSS-120B are distilled into small rerankers (1.5B, 3B). The Generator is trained with GRPO together with the reranker. - **Test-time scaling:** the relevance-guided method matched or beat naïve majority voting while using 45.2% fewer reranker calls (3B) and 53.8% fewer (7B). Each reranker call produces about 990 tokens. - **Ablations:** replacing annotations with full documents lowered results on every benchmark. Context-utilization (CUE) scores show that the gains come from better use of evidence, not only from better retrieval. - **Limitations and availability:** the second LLM adds computational overhead, and annotation errors can accumulate across retrieval steps. Experiments used only an offline retriever. Code is available at github.com/0k9d0h1/VerbalR3. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [Seoul National University](https://md.research.elamur.ai/free/en/universities/seoul-national-university/) — University, Seoul, South Korea · 43 works · 45,969 citations - [Daegu Gyeongbuk Institute of Science and Technology](https://md.research.elamur.ai/free/en/universities/daegu-gyeongbuk-institute-of-science-and-technology/) — University, Daegu, South Korea · 2 works 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works 🧭 Topics (2): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,685 works · [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 552 works ## Cite Cite as: Sangkwon Park, Donghun Kang, Jisoo Mok and Sungroh Yoon. 2026. Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{park2026verbal, title = {{Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning}}, author = {Sangkwon Park, Donghun Kang, Jisoo Mok and Sungroh Yoon}, booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, year = {2026}, doi = {10.18653/v1/2026.acl-long.1712}, url = {https://doi.org/10.18653/v1/2026.acl-long.1712} } ``` - [← previous Vector Calligrapher: Generating Scalable Vector Graphics via Structured Linguistic Supervision](https://md.research.elamur.ai/free/en/papers/zhou-2026-vector-calligrapher-generating-scalable-vector/) - [next → VerbaNexAI at ClinicalSkillQA: From Visual Evidence to Procedural Order A Two-Stage Generative Vision-Language Framework for ClinSkillQA](https://md.research.elamur.ai/free/en/papers/tovar-2026-verbanexai-clinicalskillqa-visual-evidence-procedural/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/55/) ## References 58 references, 39 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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