Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents

📰 ArXiv cs.AI

Learn how to implement bidirectional semantic complementary tool retrieval for remote sensing agents using large language models to improve tool retrieval efficiency

advanced Published 9 Jun 2026

Action Steps

Implement a bidirectional semantic complementary tool retrieval system using a large language model
Train the model on a dataset of tool documentation and natural language queries
Evaluate the model's performance using metrics such as precision and recall
Integrate the tool retrieval system with existing remote sensing workflows
Test and refine the system to improve tool retrieval efficiency

Who Needs to Know This

Data scientists and AI engineers working on remote sensing projects can benefit from this technique to improve the efficiency of their agentic workflows

Key Insight

💡 Bidirectional semantic complementary tool retrieval can help overcome the 'semantic asymmetry' bottleneck in tool retrieval for remote sensing agents

Full Article

Title: Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents

Abstract:
arXiv:2606.07538v1 Announce Type: cross Abstract: Large language model (LLM)-based agents provide a novel paradigm for the automated processing of remote sensing(RS) data. Their success in complex RS tasks rely on extensive specialized tool libraries. However, tool documentation often exceeds the context window limits of LLMs, making precise tool retrieval essential for agentic workflows. Existing tool retrieval methods face "semantic asymmetry" bottleneck: natural language queries typically exp

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