{"ID":5551953,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T04:46:53.079013169Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00454","arxiv_id":"2607.00454","title":"Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation","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.","short_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 agro...","url_abs":"https://arxiv.org/abs/2607.00454","url_pdf":"https://arxiv.org/pdf/2607.00454v1","authors":"[\"Vedant Balasubramaniam\",\"Geetha Charan\",\"Manojkumar Patil\",\"Rohit P Suresh\",\"V Priyanka\",\"Kodur Sai Vinay Sathvik\",\"Y. Narahari\"]","published":"2026-07-01T05:18:20Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.MA\"]","methods":"[\"Large Language Model\"]","has_code":false}