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ARXIV:2604.13888 · AGENTS · SUBMITTED 16 APR · 18:19 UTC · FRESHNESS STALE
ARXIV:2604.13888AGENTSSUBMITTED 16 APR · 18:19 UTCFRESHNESS STALEBo Yu · Cheng Yang · Dongyang Hou · Chengfu Liu · Jiayao Liu · Chi Wang · +3 at arXiv
A dynamic benchmark and agent architecture for evaluating and improving tool-augmented LLMs in complex spatial analysis tasks.
Opportunity summary
Pain A dynamic benchmark and agent architecture for evaluating and improving tool-augmented LLMs in complex spatial analysis tasks.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A dynamic benchmark and agent architecture for evaluating and improving tool-augmented LLMs in complex spatial analysis tasks. However, evaluating these LLM-based agents remains challenging due to the complex, multi-step nature of geospatial workflows.
The integration of Large Language Models (LLMs) into Geographic Information Systems (GIS) marks a paradigm shift toward autonomous spatial analysis. However, evaluating these LLM-based agents remains challenging due to the complex, multi-step nature of…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments with seven representative LLMs demonstrate that the Plan-and-React paradigm significantly outperforms traditional frameworks, achieving the optimal balance between logical rigor and execution…
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A dynamic benchmark and agent architecture for evaluating and improving tool-augmented LLMs in complex spatial analysis tasks.
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10.48550/arXiv.2604.13888A dynamic benchmark and agent architecture for evaluating and improving tool-augmented LLMs in complex spatial analysis tasks.
Abstract
The integration of Large Language Models (LLMs) into Geographic Information Systems (GIS) marks a paradigm shift toward autonomous spatial analysis. However, evaluating these LLM-based agents remains challenging due to the complex, multi-step nature of geospatial workflows. Existing benchmarks primarily rely on static text or code matching, neglecting dynamic runtime feedback and the multimodal nature of spatial outputs. To address this gap, we introduce GeoAgentBench (GABench), a dynamic and interactive evaluation benchmark tailored for tool-augmented GIS agents. GABench provides a realistic execution sandbox integrating 117 atomic GIS tools, encompassing 53 typical spatial analysis tasks across 6 core GIS domains. Recognizing that precise parameter configuration is the primary determinant of execution success in dynamic GIS environments, we designed the Parameter Execution Accuracy (PEA) metric, which utilizes a "Last-Attempt Alignment" strategy to quantify the fidelity of implicit parameter inference. Complementing this, a Vision-Language Model (VLM) based verification is proposed to assess data-spatial accuracy and cartographic style adherence. Furthermore, to address the frequent task failures caused by parameter misalignments and runtime anomalies, we developed a novel agent architecture, Plan-and-React, that mimics expert cognitive workflows by decoupling global orchestration from step-wise reactive execution. Extensive experiments with seven representative LLMs demonstrate that the Plan-and-React paradigm significantly outperforms traditional frameworks, achieving the optimal balance between logical rigor and execution robustness, particularly in multi-step reasoning and error recovery. Our findings highlight current capability boundaries and establish a robust standard for assessing and advancing the next generation of autonomous GeoAI.
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PROBLEM
A dynamic benchmark and agent architecture for evaluating and improving tool-augmented LLMs in complex spatial analysis tasks. However, evaluating these LLM-based agents remains challenging due to the complex, multi-step nature of geospatial workflows.
METHOD
The integration of Large Language Models (LLMs) into Geographic Information Systems (GIS) marks a paradigm shift toward autonomous spatial analysis. However, evaluating these LLM-based agents remains challenging due to the complex, multi-step nature of geospatial workflows.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments with seven representative LLMs demonstrate that the Plan-and-React paradigm significantly outperforms traditional frameworks, achieving the optimal balance between logical rigor and...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A dynamic benchmark and agent architecture for evaluating and improving tool-augmented LLMs in complex spatial analysis tasks. However, evaluating these LLM-based agents remains challenging due to the complex, multi-step nature of geospatial workflows.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The integration of Large Language Models (LLMs) into Geographic Information Systems (GIS) marks a paradigm shift toward autonomous spatial analysis. However, evaluating these LLM-based agents remains challenging due to the complex, multi-step nature of geospatial workflows.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments with seven representative LLMs demonstrate that the Plan-and-React paradigm significantly outperforms traditional frameworks, achieving the optimal balance between logical rigor and execution robustness, particularly in multi-step reasoning and error recovery. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A dynamic benchmark and agent architecture for evaluating and improving tool-augmented LLMs in complex spatial analysis tasks.
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