{"slug": "acare-agent-context-aware-reranker-for-iterative-search", "title": "ACARE: Agent-Context-Aware Reranker for Iterative Search", "summary": "Researchers submitted ACARe (Agent-Context-Aware Reranker), a compact 4B LLM trained to rerank documents using the agent's broader task and reasoning context rather than the sub-query alone, to arXiv on 26 October 2026. Across multiple benchmarks and agent families, ACARe improved retrieval quality over its base reranker while reducing search effort, and in answer generation it improved accuracy and cut searches versus no reranking while matching or exceeding the base reranker's mean accuracy with more precise evidence. The work also narrowed the retrieval-quality gap between smaller and larger agents, and the model, training data and code are slated for release.", "body_md": "# Computer Science > Information Retrieval\n\n  [Submitted on 26 Oct 2026]\n\n# Title:ACARE: Agent-Context-Aware Reranker for Iterative Search\n\n[View PDF](http://arxiv.org/pdf/2610.35840v1)\n\n[HTML (experimental)](https://arxiv.org/html/2610.35840v1)\n\nAbstract:In agentic search, a document can match an agent-generated sub-query without helping answer the user's original question. Conventional rerankers score documents against the sub-query alone, overlooking the broader task and the agent's reasoning. We introduce the Agent-Context-Aware Reranker (ACARe), a compact 4B LLM trained to rerank documents using this context when available. Across multiple benchmarks and agent families, ACARe improves retrieval quality over its base reranker while reducing search effort. These benefits extend to answer generation: ACARe improves accuracy and reduces searches over no reranking, while matching or exceeding the base reranker's mean accuracy with more precise evidence. It also helps smaller agents narrow the retrieval-quality gap to larger agents, showing how better retrieval tools can make smaller agents more effective. The model, training data, and code will be released soon.\n    \n\n## Submission history\n\nFrom: Sahel Sharifymoghaddam [\n[view email](http://arxiv.org/show-email/63a415f3/2610.35840)]\n\n**[v1]** Mon, 26 Oct 2026 17:53:30 UTC (176 KB)\n\n### Additional Features\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/acare-agent-context-aware-reranker-for-iterative-search", "canonical_source": "http://arxiv.org/abs/2610.35840v1", "published_at": "2026-10-27 15:31:24+00:00", "updated_at": "2026-10-27 15:43:45.475330+00:00", "lang": "en", "topics": ["ai-research", "ai-agents", "large-language-models", "ai-search", "artificial-intelligence"], "entities": ["ACARe", "arXiv", "Sahel Sharifymoghaddam"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/acare-agent-context-aware-reranker-for-iterative-search", "markdown": "https://wpnews.pro/news/acare-agent-context-aware-reranker-for-iterative-search.md", "text": "https://wpnews.pro/news/acare-agent-context-aware-reranker-for-iterative-search.txt", "jsonld": "https://wpnews.pro/news/acare-agent-context-aware-reranker-for-iterative-search.jsonld"}}