HiRS-Agent: A Hierarchical Multi-Agent System for Reliable Long-Horizon Remote Sensing Task Solving

• URL: http://arxiv.org/abs/2608.30672v1
• Date: Mon, 31 Aug 2026 12:13:41 GMT
• Title: HiRS-Agent: A Hierarchical Multi-Agent System for Reliable Long-Horizon Remote Sensing Task Solving
• Abstract summary: HiRS-Agent is a hierarchical multi-agent system for long-horizon RS task solving.<n>Manager Layer handles dynamic routing, step-level verification, replanning, and termination control.<n>Specialist Layer organizes domain-specific tools according to the RS workflow and is responsible for subtask reasoning and tool execution.
• Score: 24.41944797622092
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Multi-Agent Computer Use [72.79887808312706]
  We argue that we should move towards evaluating and building multi-agent computer use (MACU) systems.<n>We propose a general multi-agent setup in which a manager model decomposes computer use tasks as a directed acyclic graph (DAG)<n>At each iteration, the manager dispatches parallel CUA subagents to carry out nodes on the ready frontier of the DAG.
  arXiv  Detail & Related papers  (2026-06-01T01:29:36Z)
• Small Model as Master Orchestrator: Learning Unified Agent-Tool Orchestration with Parallel Subtask Decomposition [61.291733522717415]
  Agent-as-Tool is a unified parallel orchestration paradigm that mitigates both agents and tools into a standardized, learnable action space.<n>ParaManager decouples planning decisions from subtask solving, enabling state-aware parallel subtask decomposition, delegation, and asynchronous execution.<n>Experiments show that ParaManager achieves strong performance across multiple benchmarks and exhibits robust generalization under unseen model pools.
  arXiv  Detail & Related papers  (2026-04-18T14:41:27Z)
• OrgAgent: Organize Your Multi-Agent System like a Company [75.47076168155817]
  We introduce OrgAgent, a company-style hierarchical multi-agent framework.<n>We find that multi-agent systems organized in a company-style hierarchy generally outperform other organizational structures.
  arXiv  Detail & Related papers  (2026-04-01T15:21:14Z)
• BOAD: Discovering Hierarchical Software Engineering Agents via Bandit Optimization [41.08366028094234]
  Large language models (LLMs) struggle to generalize to real-world software engineering problems.<n>Existing systems often rely on a single agent to handle the entire workflow-interpreting issues.<n>Motivated by how human engineers decompose complex problems, we propose structuring SWE agents as orchestrators coordinating specialized sub-agents.
  arXiv  Detail & Related papers  (2025-12-29T17:41:11Z)
• Designing Domain-Specific Agents via Hierarchical Task Abstraction Mechanism [61.01709143437043]
  We introduce a novel agent design framework centered on a Hierarchical Task Abstraction Mechanism (HTAM)<n>Specifically, HTAM moves beyond emulating social roles, instead structuring multi-agent systems into a logical hierarchy that mirrors the intrinsic task-dependency graph of a given domain.<n>We instantiate this framework as EarthAgent, a multi-agent system tailored for complex geospatial analysis.
  arXiv  Detail & Related papers  (2025-11-21T12:25:47Z)
• XAgents: A Unified Framework for Multi-Agent Cooperation via IF-THEN Rules and Multipolar Task Processing Graph [14.273739638741139]
  XAgents is a unified multi-agent cooperative framework built on a multipolar task processing graph and IF-THEN rules.<n>XAgents consistently surpasses state-of-the-art single-agent and multi-agent approaches in both knowledge-typed and logic-typed question-answering tasks.
  arXiv  Detail & Related papers  (2025-09-12T08:40:58Z)
• PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC [98.82146219495792]
  In this paper, we propose a hierarchical agent framework named PC-Agent.<n>From the perception perspective, we devise an Active Perception Module (APM) to overcome the inadequate abilities of current MLLMs in perceiving screenshot content.<n>From the decision-making perspective, to handle complex user instructions and interdependent subtasks more effectively, we propose a hierarchical multi-agent collaboration architecture.
  arXiv  Detail & Related papers  (2025-02-20T05:41:55Z)
• Multi-Agent Geospatial Copilots for Remote Sensing Workflows [1.8241060496411214]
  GeoLLM-Squad introduces the novel multi-agent paradigm to remote sensing (RS)<n>Unlike existing single-agent approaches that rely on monolithic large language models (LLM), GeoLLM-Squad separates agentic orchestration from geospatial task-solving.<n>Our work enables the modular integration of diverse applications, spanning urban monitoring, forestry protection, climate analysis, and agriculture studies.
  arXiv  Detail & Related papers  (2025-01-27T17:54:31Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems.<n>In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy.<n> Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent [15.836845304125436]
  RS-Agent is an AI agent designed to interact with human users and autonomously leverage specialized models.<n> RS-Agent integrates four key components: a Central Controller based on large language models, a dynamic toolkit for tool execution, a Solution Space for task-specific expert guidance, and a Knowledge Space for domain-level reasoning.<n>Extensive experiments across 9 datasets and 18 remote sensing tasks demonstrate that RS-Agent significantly outperforms state-of-the-art MLLMs.
  arXiv  Detail & Related papers  (2024-06-11T09:30:02Z)
• TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation [41.21899915378596]
  We propose a multi-agent framework based on dynamic Task Decomposition and Agent Generation (TDAG)<n>This framework dynamically decomposes complex tasks into smaller subtasks and assigns each to a specifically generated subagent.<n>ItineraryBench is designed to assess agents' abilities in memory, planning, and tool usage across tasks of varying complexity.
  arXiv  Detail & Related papers  (2024-02-15T18:27:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.