{"slug": "strategist-evolving-reasoning-with-agents", "title": "Strategist: Evolving Reasoning with Agents", "summary": "A November 2, 2026 arXiv paper introduces Strategist, a meta-agent that evolves inference strategies by writing them as reusable executable \"blueprints\" and revising them against real samples. Across 30 benchmarks, Strategist beat the strongest baseline on 24 and matched its average accuracy (74.7% versus 74.0%) at 39% lower inference cost, with strategies transferring to unseen tasks and other models without further search. The work targets the hand-written half of agent systems — the reasoning procedure that turns model calls into a response — and lets that layer improve on its own.", "body_md": "# Computer Science > Artificial Intelligence\n\n  [Submitted on 2 Nov 2026]\n\n# Title:Strategist: Evolving Reasoning with Agents\n\n[View PDF](http://arxiv.org/pdf/2611.03618v1)\n\n[HTML (experimental)](https://arxiv.org/html/2611.03618v1)\n\nAbstract:A model is only half of an AI agent system. The other half is the inference strategy: the reasoning procedure that turns model calls into a response. Today that second half is written by hand, and the experience of building one strategy never carries to the next. We introduce Strategist, a meta-agent that evolves inference strategies across tasks. Each strategy is written as a blueprint,executable code that other strategies can reuse, and together the blueprints form a library. Given a task and a small development set, Strategist retrieves related blueprints, writes candidate strategies, tests them on real samples, and revises them from the results. Every candidate then enters the library, so later tasks build on earlier discoveries. On 30 benchmarks, Strategist beats the strongest baseline on 24 and matches its average accuracy (74.7% vs.74.0%) at 39% lower inference cost. Its strategies also transfer to unseen tasks and other models without further search. The part of an AI system that is not improved on its own now does.\n    \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/strategist-evolving-reasoning-with-agents", "canonical_source": "http://arxiv.org/abs/2611.03618v1", "published_at": "2026-11-03 15:24:38+00:00", "updated_at": "2026-11-03 15:55:13.289496+00:00", "lang": "en", "topics": ["ai-agents", "ai-research", "artificial-intelligence", "large-language-models", "machine-learning"], "entities": ["Strategist", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/strategist-evolving-reasoning-with-agents", "markdown": "https://wpnews.pro/news/strategist-evolving-reasoning-with-agents.md", "text": "https://wpnews.pro/news/strategist-evolving-reasoning-with-agents.txt", "jsonld": "https://wpnews.pro/news/strategist-evolving-reasoning-with-agents.jsonld"}}