AgriWorld:A World Tools Protocol Framework for Verifiable Agricultural Reasoning with Code-Executing LLM Agents

Zhixing Zhang; Jesen Zhang; Hao Liu; Qinhan Lv; Jing Yang; Kaitong Cai; Keze Wang

cs.AIFeb 17, 2026

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

Organizations: Sun Yat-sen University

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 code, observes execution results, and refines its analysis via an execute-observe-refine loop. We introduce AgroBench, with scalable data generation for diverse agricultural QA spanning lookups, forecasting, anomaly detection, and counterfactual "what-if" analysis. Experiments outperform text-only and direct tool-use baselines, validating execution-driven reflection for reliable agricultural reasoning.

Figures & tables

Figure 1: Overview of the proposed Framework. The pipeline begins with a heterogeneous State Space $\mathcal{S}$ (e.g., geographical locations, wind fields). Data flows through functional modules such as Spatial Query , Spatio-temporal Analysis , and Crop Simulation . A critical Coordinate Alignment step transforms the raw Pre-training Sequence into a unified Aligned Sequence ( $g\to kg$ ). Finally, the Agent validates the timeline for Temporal Errors and enforces constraints like Pattern Validity and Numerical Conservation before producing the Final Output .

Tool Family	Illustrative Signatures
Geospatial Querying	geo.filter_parcels(region, crop, area_range) geo.sjoin(parcels, sensor_footprints)
Remote-Sensing Analytics	rs.load(index, time_range, region) rs.zonal_stats(raster_ts, parcels, stat={mean,...}) rs.anomaly(ts, baseline_window, method)
Soil/Terrain Access	grid.sample(gridfield, parcels) grid.aggregate(gridfield, region)
Weather Variables	wx.get(station/region, time_range) wx.rolling_sum/degree_days(...)
Simulation & Predictors	sim.run(parcels, wx, soil, mgmt, intervention) pred.stress/yield/disease(...)

Table 1: The AgriWorld Toolset. Grouping of core APIs by functional domain. The system exposes unified interfaces for heterogeneous spatiotemporal data.

Method	Interaction	Tool Scope	Alignment	Avg. Turns	Acc (%)
Baselines
(1) Text-Only (Kimi/GPT)	Chat	None	–	1.0	21.9
(2) AgriWorld-Direct	One-shot	All	✓	1.0	48.2
Agro-Reflective Variants (Ours)
(3) w/o Remote Sensing	Reflective	w/o RS	✓	2.4	41.5
(4) w/o Alignment	Reflective	All	$\times$	3.1	51.3

Table 2: Ablation and design space exploration of Agro-Reflective. We compare against text-only and direct-execution baselines. ‘Alignment’ denotes the spatiotemporal canonical alignment mechanism. Bold indicates best performance.

Model	Animal	Plant	Aquatic	Herb	QA Total	Choice (%)
Ours (Agro-Reflective)
Qwen3-32B-LoRA-Reflective	7.45/118	7.76/325	7.68/74	8.66/24	7.72/541	73.84
Qwen3-8B-LoRA-Reflective	6.08/118	6.41/325	5.92/74	5.98/24	6.25/541	41.34
Base Models (LoRA Tuned)
Qwen3-32B-LoRA	7.21/118	7.45/325	7.28/74	8.46/24	7.42/541	67.25
Qwen3-8B-LoRA	5.97/118	5.71/325	5.82/74	7.04/24	5.84/541	57.55

Table 3: Main Results on Agricultural Reasoning Benchmarks. We report the aggregated score (Score/Total num Tasks) for domain-specific QA tasks and the percentage accuracy (%) for multiple-choice tasks. Bold denotes the best performance.

Model	Lookup	Forecasting	Anomaly	Counterfactual
Model	Acc.	NRMSE ( $\downarrow$ )	IoU ( $\uparrow$ )	Success ( $\uparrow$ )
Text-Only	41.2%	0.89	0.12	14.5%
One-shot	82.5%	0.34	0.51	43.8%
Reflective	86.7%	0.18	0.68	71.4%

Table 4: Task-Specific Performance Breakdown. Agro-Reflective shows the largest gains in complex multi-step reasoning tasks (Forecasting, Counterfactual) compared to the One-shot baseline.

Panel of Figure 2 — Figure 2: Performance Scaling. Accuracy saturates logarithmically, showing efficient self-correction.

Method	IID	Spatial OOD		Temporal OOD
Method	Acc.	Acc.	$\Delta$	Acc.	$\Delta$
Text-Only	41.2	18.5	-55%	22.1	-46%
One-shot	48.2	26.4	-45%	35.8	-26%
Agro-Reflective	73.8	64.5	-12%	69.2	-6%

Table 5: OOD Robustness Analysis. Comparison of accuracy degradation (

\Delta

) on unseen regions (Spatial) and years (Temporal). Text models fail to generalize, while tool-grounded agents remain robust.

Tool-Augmented Language Model Agents

Tool-augmented language model agents use external tools, APIs, and environments to solve tasks beyond text generation alone.

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Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B

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1Sanya University · 2Hebei International Studies University

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General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text generation. Agricultural applications, however, are domain-specific, region-dependent, time-sensitive, and safety-critical. Without data governance, expert evaluation, and evidence constraints, an agricultural assistant mayproduce unreliable advice on crop diseases, pesticide use, fertilization, or policy interpretation.To avoid presenting unverified simulated numbers as real experimental findings, this paper doesnot report any model-performance claims that have not been produced by an actual training runand expert evaluation. Instead, we propose AgriTune-R, a reproducible and auditable frameworkfor adapting general-purpose LLMs to agricultural tasks. The framework selects the publiclyverifiable Qwen3-8B model as the recommended base model and integrates agricultural datagovernance, instruction construction, LoRA/QLoRA parameter-efficient fine-tuning, retrievalaugmented generation, expert evaluation, and safety control for high-risk questions. The contributions are: (1) a structured workflow for agricultural LLM adaptation; (2) an evaluationprotocol for agricultural knowledge QA, pest and disease consultation, cultivation management,and policy explanation; (3) an expert-review rubric combining factuality, safety, evidence consistency, and uncertainty expression; and (4) a clear separation between protocol design andempirical conclusions, providing an executable baseline for future empirical studies.

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