[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-APSIM":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"APSIM","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":15,"published_at":18,"category":19,"cover_url":15,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":30,"tags":32,"primary_topic":15,"secondary_topics":38,"classification":39,"search_phrases":52,"slug":55,"view_count":27,"doi":15,"paper":15,"created_at":56},56,"Agri-SAGE：基于模拟的多智能体大语言模型用于上下文感知农业咨询生成","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454","Agri-SAGE是一个闭环框架，旨在通过将检索增强的多智能体LLM推理与基于APSIM的生物物理模拟相结合，生成并验证农艺建议，从而解决静态农艺指南\"对季节内变化和动态不确定性视而不见\"以及LLM驱动咨询系统\"生成农艺上可信但生理上不可信的建议\"两个核心矛盾。研究评估了三种推理方法——规划与求解、思维树和反思法，进行了为期10年的回顾性分析。结果显示三种推理方法均优于静态基线，其中思维树实现最高产量，反思法以较低计算成本达到相当效果。",null,"[Skip to main content](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454#content)\n\n![Image 1](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ficons\u002Fsmileybones-small.svg)arXiv is now an independent nonprofit![Learn more](https:\u002F\u002Finfo.arxiv.org\u002Fabout)×\n\n[](https:\u002F\u002Farxiv.org\u002FIgnoreMe)[![Image 2: archive](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Farxiv-logo-primary-light.svg)](https:\u002F\u002Farxiv.org\u002F)[Search](https:\u002F\u002Farxiv.org\u002Fsearch)[Submit](https:\u002F\u002Farxiv.org\u002Fuser\u002Fcreate)[Donate](https:\u002F\u002Finfo.arxiv.org\u002Fabout\u002Fdonate.html)[Log in](https:\u002F\u002Farxiv.org\u002Flogin)\n\nSearch arXiv \n\n Press Enter to search · [Advanced search](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fadvanced)\n\n# Computer Science > Artificial Intelligence\n\n**arXiv:2607.00454** (cs) \n\n [Submitted on 1 Jul 2026]\n\n# Title:Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation\n\nAuthors:[Vedant Balasubramaniam](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Balasubramaniam,+V), [Geetha Charan](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Charan,+G), [Manojkumar Patil](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Patil,+M), [Rohit P Suresh](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Suresh,+R+P), [V Priyanka](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Priyanka,+V), [Kodur Sai Vinay Sathvik](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Sathvik,+K+S+V), [Y. Narahari](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Narahari,+Y)\n\nView a PDF of the paper titled Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation, by Vedant Balasubramaniam and Geetha Charan and Manojkumar Patil and Rohit P Suresh and V Priyanka and Kodur Sai Vinay Sathvik and Y. Narahari\n\n[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2607.00454)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2607.00454v1)\n> Abstract:Agricultural advisory systems face a fundamental tension: static agronomic guidelines offer consistent, evidence-based recommendations, yet remain blind to in-season variability and dynamic uncertainties. Recent advisory systems powered by LLMs are liable for a different risk of generating recommendations that are agronomically credible but physiologically unconvincing. Agri-SAGE is a closed-loop framework designed to resolve the above two limitations by integrating retrieval-grounded multi-agent LLM reasoning with APSIM-based biophysical simulation, to generate and validate agronomic advisories. To assess this framework, we evaluate three reasoning approaches, namely Plan-and-Solve, Tree of Thoughts, and Reflexion, over a 10-year retrospective analysis. All three significantly outperform static PoP (Package-of-Practice) baselines, with Tree of Thoughts achieving impressive peak yields. At the same time, Reflexion achieves comparable agronomic outcomes at substantially lower computational cost by leveraging cross-seasonal episodic memory.\n\nSubjects:Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)\nCite as:[arXiv:2607.00454](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454) [cs.AI]\n(or [arXiv:2607.00454v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454v1) [cs.AI] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.00454](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.00454)\n\nFocus to learn more\n\n arXiv-issued DOI via DataCite\n\n## Submission history\n\n From: Geetha Charan [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002Fe08e6969\u002F2607.00454)] \n\n**[v1]** Wed, 1 Jul 2026 05:18:20 UTC (583 KB)\n\n[](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454)Full-text links:\n## Access Paper:\n\n View a PDF of the paper titled Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation, by Vedant Balasubramaniam and Geetha Charan and Manojkumar Patil and Rohit P Suresh and V Priyanka and Kodur Sai Vinay Sathvik and Y. Narahari\n\n*   [View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2607.00454)\n*   [HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2607.00454v1)\n*   [TeX Source](https:\u002F\u002Farxiv.org\u002Fsrc\u002F2607.00454)\n\n[![Image 3: license icon](https:\u002F\u002Farxiv.org\u002Ficons\u002Flicenses\u002Fby-4.0.png)view license](http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F \"Rights to this article\")\n\n### Current browse context:\n\ncs.AI\n\n[\u003Cprev](https:\u002F\u002Farxiv.org\u002Fprevnext?id=2607.00454&function=prev&context=cs.AI \"previous in cs.AI (accesskey p)\") | [next>](https:\u002F\u002Farxiv.org\u002Fprevnext?id=2607.00454&function=next&context=cs.AI \"next in cs.AI (accesskey n)\")\n\n[new](https:\u002F\u002Farxiv.org\u002Flist\u002Fcs.AI\u002Fnew) | [recent](https:\u002F\u002Farxiv.org\u002Flist\u002Fcs.AI\u002Frecent) | [2026-07](https:\u002F\u002Farxiv.org\u002Flist\u002Fcs.AI\u002F2026-07)\n\n Change to browse by: \n\n[cs](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454?context=cs)\n\n[cs.MA](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454?context=cs.MA)\n\n### References & Citations\n\n*   [NASA ADS](https:\u002F\u002Fui.adsabs.harvard.edu\u002Fabs\u002FarXiv:2607.00454)\n*   [Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar_lookup?arxiv_id=2607.00454)\n*   [Semantic Scholar](https:\u002F\u002Fapi.semanticscholar.org\u002FarXiv:2607.00454)\n\nexport BibTeX citation Loading...\n\n## BibTeX formatted citation\n\n×\n\nData provided by: [](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454)\n\n### Bookmark\n\n[![Image 4: BibSonomy](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbrowse\u002F0.3.4\u002Fimages\u002Ficons\u002Fsocial\u002Fbibsonomy.png)](http:\u002F\u002Fwww.bibsonomy.org\u002FBibtexHandler?requTask=upload&url=https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454&description=Agri-SAGE:%20Simulation-Grounded%20Multi-Agent%20LLM%20for%20Context-Aware%20Agricultural%20Advisory%20Generation \"Bookmark on BibSonomy\")[![Image 5: Reddit](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbrowse\u002F0.3.4\u002Fimages\u002Ficons\u002Fsocial\u002Freddit.png)](https:\u002F\u002Freddit.com\u002Fsubmit?url=https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454&title=Agri-SAGE:%20Simulation-Grounded%20Multi-Agent%20LLM%20for%20Context-Aware%20Agricultural%20Advisory%20Generation \"Bookmark on Reddit\")\n\nBibliographic Tools \n\n# Bibliographic and Citation Tools\n\n- [x] Bibliographic Explorer Toggle \n\nBibliographic Explorer _([What is the Explorer?](https:\u002F\u002Finfo.arxiv.org\u002Flabs\u002Fshowcase.html#arxiv-bibliographic-explorer))_\n\n- [x] Connected Papers Toggle \n\nConnected Papers _([What is Connected Papers?](https:\u002F\u002Fwww.connectedpapers.com\u002Fabout))_\n\n- [x] Litmaps Toggle \n\nLitmaps _([What is Litmaps?](https:\u002F\u002Fwww.litmaps.co\u002F))_\n\n- [x] scite.ai Toggle \n\nscite Smart Citations _([What are Smart Citations?](https:\u002F\u002Fwww.scite.ai\u002F))_\n\nCode, Data, Media \n\n# Code, Data and Media Associated with this Article\n\n- [x] alphaXiv Toggle \n\nalphaXiv _([What is alphaXiv?](https:\u002F\u002Falphaxiv.org\u002F))_\n\n- [x] Links to Code Toggle \n\nCatalyzeX Code Finder for Papers _([What is CatalyzeX?](https:\u002F\u002Fwww.catalyzex.com\u002F))_\n\n- [x] DagsHub Toggle \n\nDagsHub _([What is DagsHub?](https:\u002F\u002Fdagshub.com\u002F))_\n\n- [x] GotitPub Toggle \n\nGotit.pub _([What is GotitPub?](http:\u002F\u002Fgotit.pub\u002Ffaq))_\n\n- [x] Huggingface Toggle \n\nHugging Face _([What is Huggingface?](https:\u002F\u002Fhuggingface.co\u002Fhuggingface))_\n\n- [x] ScienceCast Toggle \n\nScienceCast _([What is ScienceCast?](https:\u002F\u002Fsciencecast.org\u002Fwelcome))_\n\nDemos \n\n# Demos\n\n- [x] Replicate Toggle \n\nReplicate _([What is Replicate?](https:\u002F\u002Freplicate.com\u002Fdocs\u002Farxiv\u002Fabout))_\n\n- [x] Spaces Toggle \n\nHugging Face Spaces _([What is Spaces?](https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fspaces))_\n\n- [x] Spaces Toggle \n\nTXYZ.AI _([What is TXYZ.AI?](https:\u002F\u002Ftxyz.ai\u002F))_\n\nRelated Papers \n\n# Recommenders and Search Tools\n\n- [x] Link to Influence Flower \n\nInfluence Flower _([What are Influence Flowers?](https:\u002F\u002Finfluencemap.cmlab.dev\u002F))_\n\n- [x] Core recommender toggle \n\nCORE Recommender _([What is CORE?](https:\u002F\u002Fcore.ac.uk\u002Fservices\u002Frecommender))_\n\n*   [Author](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454)\n*   [Venue](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454)\n*   [Institution](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454)\n*   [Topic](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00454)\n\n About arXivLabs  \n\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:\u002F\u002Finfo.arxiv.org\u002Flabs\u002Findex.html).\n\n[Which authors of this paper are endorsers?](https:\u002F\u002Farxiv.org\u002Fauth\u002Fshow-endorsers\u002F2607.00454) | [Disable MathJax](javascript:setMathjaxCookie()) ([What is MathJax?](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fmathjax.html)) \n\n We gratefully acknowledge support from our **major funders**, [**member institutions**](https:\u002F\u002Finfo.arxiv.org\u002Fabout\u002Fourmembers.html), , and all contributors. \n\n[About](https:\u002F\u002Finfo.arxiv.org\u002Fabout)·[Help](https:\u002F\u002Finfo.arxiv.org\u002Fhelp)·[Contact](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fcontact.html)·[Subscribe](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fsubscribe)·[Copyright](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Flicense\u002Findex.html)·[Privacy](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fpolicies\u002Fprivacy_policy.html)·[Accessibility](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fweb_accessibility.html)·[Operational Status (opens in new tab)](https:\u002F\u002Fstatus.arxiv.org\u002F)\n\nMajor funding support from\n\n[![Image 6: Simons Foundation](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fsimons-foundation.png)](https:\u002F\u002Fwww.simonsfoundation.org\u002F)[![Image 7: Simons Foundation International](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fsimons-foundation-international.png)](https:\u002F\u002Fwww.sfi.org.bm\u002F)[![Image 8: Schmidt Sciences](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fschmidt-sciences.png)](https:\u002F\u002Fwww.schmidtsciences.org\u002F)","arXiv:2607.00454v1 [cs.AI] · 2026-07-02","2026-07-01T20:00:00Z","论文",10,false,76,{"impact":24,"substance":25,"depth":26,"authority":27,"freshness":28,"relevant":7,"comment":29},20,22,18,14,2,"提出结合多智能体LLM与APSIM仿真的农业咨询框架，方法新颖，但论文发布于近一个月前，时效性低。",[31],{"name":17,"url":13},[19,33,34,35,36,4,37],"预印本","农业咨询","大语言模型","多智能体","模拟",[],{"status":40,"locked":21,"version":41,"method":42,"evidence":43},"unclassified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":50,"reason":51},[45],{"code":46,"score":28,"supported":21,"title":47,"abstract":48,"tags":49},"I2",[],[],[36,35],true,"缺少足够的主题证据",[53,54],"大语言模型 农业咨询 多智能体 预印本","大语言模型 农业咨询","大语言模型农业咨询多智能体预印本-56","2026-07-28T23:53:31.334067Z"]