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[2602.15325] AgriWorld:A World Tools Protocol Framework for Verifiable Agricultural Reasoning with Code-Executing LLM Agents
Summary
The AgriWorld framework integrates large language models with agricultural data processing, enhancing reasoning capabilities for agronomic workflows through an interactive code-executing agent.
Why It Matters
This research addresses the limitations of existing agricultural models by combining language-based reasoning with high-dimensional data analysis. It provides a novel approach to improve decision-making in agriculture, which is crucial for addressing food security and sustainable farming practices.
Key Takeaways
- AgriWorld bridges the gap between LLMs and agricultural data analysis.
- The framework enables interactive reasoning through a code-executing agent.
- Experiments show improved performance in agricultural tasks compared to traditional models.
- AgroBench facilitates diverse agricultural question answering and scenario analysis.
- The approach is significant for enhancing real-world agronomic workflows.
Computer Science > Artificial Intelligence arXiv:2602.15325 (cs) [Submitted on 17 Feb 2026] Title:AgriWorld:A World Tools Protocol Framework for Verifiable Agricultural Reasoning with Code-Executing LLM Agents Authors:Zhixing Zhang, Jesen Zhang, Hao Liu, Qinhan Lv, Jing Yang, Kaitong Cai, Keze Wang View a PDF of the paper titled AgriWorld:A World Tools Protocol Framework for Verifiable Agricultural Reasoning with Code-Executing LLM Agents, by Zhixing Zhang and 6 other authors View PDF HTML (experimental) Abstract:Foundation models for agriculture are increasingly trained on massive spatiotemporal data (e.g., multi-spectral remote sensing, soil grids, and field-level management logs) and achieve strong performance on forecasting and monitoring. However, these models lack language-based reasoning and interactive capabilities, limiting their usefulness in real-world agronomic workflows. Meanwhile, large language models (LLMs) excel at interpreting and generating text, but cannot directly reason over high-dimensional, heterogeneous agricultural datasets. We bridge this gap with an agentic framework for agricultural science. It provides a Python execution environment, AgriWorld, exposing unified tools for geospatial queries over field parcels, remote-sensing time-series analytics, crop growth simulation, and task-specific predictors (e.g., yield, stress, and disease risk). On top of this environment, we design a multi-turn LLM agent, Agro-Reflective, that iteratively writes cod...
