解决遥感智能体工具检索语义错配问题，提升复杂任务精准度。
Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents
- 通过任务分解增强查询语义，补充工具功能细节。
- 构建动态依赖图，注入前置工具上下文信息。
- 在遥感与通用任务中均显著提升检索准确率。
基于大语言模型（LLM）的智能体为遥感（RS）数据自动化处理提供了新范式，其在复杂任务中的成功依赖于丰富的专用工具库。然而，工具文档常超出LLM上下文窗口限制，精确工具检索成为智能体工作流的关键。现有方法存在“语义不对称”瓶颈：自然语言查询多表达高层意图，缺乏工具特异性语义；而工具文档则提供细粒度技术描述，缺乏工作流操作上下文。为此，本文提出双向语义互补工具检索方法。首先，在查询端引入基于规划的查询增强机制，利用智能体推理能力将抽象意图分解为逻辑子任务，主动补充缺失的功能语义。其次，在工具端，针对遥感工具链强耦合特性，构建具备持续学习能力的动态工具依赖图，通过邻域信息聚合机制，显式将前序工具上下文注入当前节点表示，丰富工具描述的上下文语义。在遥感数据集GeoPlan-bench和通用数据集API-Bank上的实验表明，该方法不仅显著提升复杂遥感任务的工具检索准确率，且对通用领域任务具有良好可迁移性。源代码与数据集见https://github.com/geox-lab/BSCTR。
原文摘要 · Abstract (English)
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 express macro-level intentions lacking tool-specific semantics, while tool documentation provides fine-grained technical descriptions lacking operational context for workflows. To bridge this semantic gap, this paper proposes a bidirectional semantic complementary tool retrieval method. First, on the query side, we introduce a planning-based query enhancement mechanism that leverages the reasoning capabilities of agents to decompose abstract intentions into logical subtasks, thereby actively supplementing the query with missing functional semantics. Second, on the tool side, addressing the strong coupling characteristics of RS tool chains, we construct a dynamic tool dependency graph with continual learning capabilities. By employing a neighborhood information aggregation mechanism, contextual information from precursor tools is explicitly injected into the current node representation, enriching tool descriptions with contextual semantics. Experimental results on the RS dataset GeoPlan-bench and the general-purpose dataset API- Bank demonstrate that the proposed method not only significantly improves tool retrieval accuracy for complex RS tasks but also exhibits robust extensibility for transfer to general-domain tasks. The source code and dataset are available at https://github.com/geox-lab/BSCTR.
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