HiRS-Agent: A Hierarchical Multi-Agent System for Reliable Long-Horizon Remote Sensing Task Solving
cs.AI, cs.MA, cs.MM
Submitted: 2026-08-31
Updated: 2026-08-31
Comments: Accepted at ACM Multimedia 2026 (MM '26)
Code: https://github.com/IntelliSensing/HiRS-Agent
License: http://creativecommons.org/licenses/by/4.0/
The gist: Recent advances in large language models and multimodal models have pushed remote sensing (RS) processing from simple perception models to agentic systems designed to tackle complex, long-horizon RS
Terminology
Abstract
Recent advances in large language models and multimodal models have pushed remote sensing (RS) processing from simple perception models to agentic systems designed to tackle complex, long-horizon RS tasks. However, existing systems often rely on monolithic decision-making frameworks, which fail to accommodate the multi-stage, interdependent nature of RS tasks. This centralized approach leads to challenges such as unstable task execution, incorrect tool usage, and error propagation across stages. To address these issues, we propose HiRS-Agent, a hierarchical multi-agent system for long-horizon RS task solving. HiRS-Agent adopts a two-level collaborative architecture: the Manager Layer handles dynamic routing, step-level verification, replanning, and termination control, while the Specialist Layer organizes domain-specific tools according to the RS workflow and is responsible for subtask reasoning and tool execution. To further enhance the system's capability, we introduce a two-stage supervised tuning strategy and a verification-guided hierarchical reinforcement learning stage to jointly optimize coordination and tool-use policies. Experiments on Earth-Agent Benchmark and ThinkGeo show that HiRS-Agent substantially improves long-horizon tool-use capability and final-task correctness, demonstrating the effectiveness of structured multi-agent collaboration for reliable RS agents. The code is publicly available at https://github.com/IntelliSensing/HiRS-Agent.
Sources
- CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications
- Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following
- RingMo-Agent: A Unified Remote Sensing Foundation Model for Multi-Platform and Multi-Modal Reasoning
- Designing Domain-Specific Agents via Hierarchical Task Abstraction Mechanism
- ToolRL: Reward is All Tool Learning Needs
- ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks
- OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Do Large Language Model Benchmarks Test Reliability?
- Agent-as-Tool: A Study on the Hierarchical Decision Making with Reinforcement Learning
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