Source-linked AI summary
HiRS-Agent: A Hierarchical Multi-Agent System for Reliable Long-Horizon Remote Sensing Task Solving
Boyang Mu, Zhiwei Wei, Mugen Peng, Wenjia Xu
TL;DR
Existing RS agents struggle with reliable long-horizon execution because centralized or weakly structured systems inadequately handle multistage dependencies, tool use, verification, and recovery. HiRS-Agent introduces hierarchical Manager and Specialist layers, workflow-aligned tools, Expert-tuning, and verification-guided hierarchical reinforcement learning; experiments on Earth-Bench and ThinkGeo report consistent gains on lightweight backbones.
Problem
Existing RS agents insufficiently address reliable long-horizon execution in realistic, multistage workflows, where early errors can propagate downstream.
Method
HiRS-Agent combines Manager-level planning, routing, verification, and recovery with Specialist-level domain reasoning and tool execution, optimized through Expert-tuning and VG-HRL.
Results
HiRS-Agent consistently improves long-horizon tool-use capability and final-task performance on Earth-Bench and ThinkGeo, including lightweight open-source backbones.
Takeaways & Limitations
Structured multi-agent collaboration improves reliable long-horizon RS task solving over lightweight open-source backbones.
Takeaways & Limitations
The fallback threshold for low-variance groups is deferred to the supplementary material.
Abstract
from arXiv · showhide
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.
1 Introduction
Existing RS agents remain insufficiently reliable for long-horizon workflows because they do not adequately model stage dependencies, RS expertise, or execution control. HiRS-Agent addresses these gaps with hierarchical coordination, workflow-aligned specialists, verification, and dedicated training.
- Existing RS agents mainly demonstrate agentization feasibility, while reliable long-horizon execution in realistic workflows remains insufficiently addressed.
- Current systems exhibit workflow, knowledge, and control mismatches involving stage dependencies, RS expertise, and intermediate-state verification.
- HiRS-Agent uses a Manager Layer for global planning and workflow control and a Specialist Layer for domain-specialized reasoning and tool execution.
- The Specialist Layer follows the RS processing chain, while the Manager Layer verifies intermediate results and can reroute, replan, or repair workflows under RS-specific constraints.
- Expert-tuning injects RS expertise and aligns task intent with executable workflows, while VG-HRL jointly optimizes global coordination and local tool execution.
2 Related Work
Prior work progressed from general reasoning and reflection to RS-oriented multimodal assistants, executable agents, and multi-agent systems. However, reliable long-horizon RS execution still lacks robust verification, adaptive recovery, and process-level control.
- ReAct and Reflexion established iterative reasoning, acting, and feedback-conditioned reflection for language-model agents.
- Plan-first reasoning and Tree-of-Thoughts showed that explicit process modeling can improve long-horizon decision quality beyond direct prompting.
- RS multimodal systems improved perception, instruction following, multimodal alignment, and reasoning across heterogeneous sensors and platforms.
- Executable RS agents expanded tool integration and workflow coverage, but many still use shallow chains, static prompts, fixed templates, or limited recovery mechanisms.
- Multi-agent research emphasizes modularity, role specialization, and coordination, while geospatial work demonstrates hierarchical coordinator–worker organizations for spatial analysis.
- Process-level reinforcement learning addresses the coarseness of final-answer supervision by optimizing tool invocation, collaboration, and hierarchical decision policies.
3 Methodology
HiRS-Agent organizes remote-sensing task solving around workflow dependencies through a Manager Layer for global control and Specialist agents for stage-specific reasoning and tool execution. Expert-tuning and VG-HRL further align domain knowledge, workflow procedures, coordination, and tool-use policies.
- Architecture: HiRS-Agent uses a two-level architecture that organizes task solving according to remote-sensing workflow dependencies.The Manager Layer maintains global planning, routing, verification, and control, while Specialist agents execute domain-specific subtasks.
- Manager Layer: The Manager Layer decomposes tasks, routes sub-goals, retrieves structured history, and selects actions such as continuing, replanning, verifying, or terminating.Its structured global memory stores task context, routing states, execution traces, and intermediate evidence for history-aware decisions.
- Verification and control: Intermediate outputs are verified for schema validity, sub-goal consistency, and physical, spectral, and statistical constraint satisfaction.FAIL triggers repair or rerouting, UNCERTAIN triggers cross-checking, and PASS commits the result to memory before proceeding.
- Specialist Layer: The Specialist Layer contains Surface Parsing, Physical Retrieval, and Statistical Analytics agents, each operating over a focused, stage-aligned tool set.The organization follows the progression from spectral parsing to physical inversion and statistical analysis, reducing cross-domain tool confusion.
- Training: Expert-tuning injects structured remote-sensing knowledge before aligning natural-language task intent with executable processing procedures.It is a two-stage supervised fine-tuning strategy designed for workflow-driven remote-sensing tasks.
- Training: VG-HRL jointly optimizes trajectory-level Manager coordination and step-level Specialist tool execution using separate hierarchical rewards and role-aware grouped optimization.The reward design distinguishes coordination from local execution, while tool-use rewards differentiate hallucinated, wrong in-domain, and correct tools.
4 Experiments
HiRS-Agent improves long-horizon remote-sensing agent performance across Earth-Bench and ThinkGeo, while Expert-tuning strengthens RS expertise and largely preserves general capability. Ablations attribute gains to workflow alignment, verification, hierarchical specialization, and VG-HRL.
- Main Results: HiRS-Agent consistently improves small open-source backbones across Earth-Bench and ThinkGeo, narrowing the gap to proprietary models as capacity increases from 4B to 8B.The reported gains are attributed mainly to hierarchical specialization, verification-guided control, and post-SFT optimization.
- Main Results: 43.95/45.56 Accuracy and 31.67/34.64 Tool-Exact-Match are reached by Qwen3-4B HiRS-Agent on Earth-Bench under AP/IF, versus 15.73/10.08 and 0.00/8.63 for vanilla Qwen3-4B.Param-Match rises from 0.00/4.21 to 19.54/20.81, while Efficiency falls from 3.9747/5.4554 to 2.5059/3.3013.
- Main Results: 77.97/59.57 Inst./Tool. and 12.09/14.75 Ans./Ans_I are achieved by Qwen3-8B HiRS-Agent on ThinkGeo, compared with 20.98/13.36 and 7.67/8.68 for the corresponding vanilla backbone.HiRS-Agent also achieves the best Ans. score among all compared methods.
- RS Domain Adaptation: 87.60 overall score on RS-EXPERT-BENCHMARK improves from 73.35 for Qwen3-4B after Expert-tuning, with gains on both in-domain and out-of-domain splits.MMLU-Redux remains nearly unchanged, while MATH-500 and Multi-IF show only moderate drops.
- Ablation Studies: 41.10/46.34 In-Order, 31.67/34.64 Exact, and 43.95/45.56 Accuracy are achieved after VG-HRL, which further improves all reported metrics beyond Expert-tuning.The ablation results associate the improvement with better ordered and exact execution.
- Ablation Studies: Adding verification raises Exact from 6.84/19.42 to 29.05/30.14, while RS-specialist grouping raises Accuracy from 30.24/33.47 to 43.95/45.56.Removing verification from the full system reduces Exact by 11.30/12.43 and Accuracy by 4.03/2.82.
5 Conclusion
HiRS-Agent uses hierarchical coordination, workflow-aligned specialization, and verification-guided optimization for reliable long-horizon remote sensing task solving. Experiments show consistent improvements in tool-use capability and final-task performance, including advantages over closed-source models in some settings.
- 5 Conclusion: HiRS-Agent combines Manager–Specialist organization, structured task decomposition, dynamic coordination, and domain-specialized tool execution for multi-stage RS workflows.Its design models workflow structure and stage dependency rather than relying on model scale alone.
- 5 Conclusion: HiRS-Agent improves reliability through RS expertise alignment, workflow alignment, step-level verification, expert-tuning, and verification-guided hierarchical reinforcement learning.
- 5 Conclusion: Experiments on Earth-Bench and ThinkGeo consistently improve long-horizon tool-use capability and final-task performance on lightweight open-source backbones.
- 5 Conclusion: A lightweight 4B open-source backbone outperforms the strongest closed-source models in certain settings, highlighting the importance of explicit workflow and stage-dependency modeling.
- 5 Conclusion: Future work will extend HiRS-Agent toward broader tools and more realistic scenarios.