RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation
Quick Answer
RSMeM introduces a knowledge-enhanced memory evolution mechanism for remote sensing agents, improving tool-use performance by 6% on DeepSeek-V3.2 with less than 1% additional experience tokens.
Quick Take
This system integrates hierarchical knowledge grounding and failure-aware experience refinement to enhance domain-specific workflows, addressing the limitations of general-purpose in geoscience applications.
Key Points
- RSMeM consists of hierarchical knowledge grounding and failure-aware experience refinement.
- It improves end-to-end answer accuracy across diverse LLM backbones.
- Extensive experiments on EarthBench validate RSMeM's effectiveness.
- The system distills failure-annotated traces into reusable constraints.
- Code for RSMeM is publicly available for further research.
DeepSignal Analysis
What happened
RSMeM is a new mechanism designed to enhance the performance of remote sensing agents by integrating domain-specific knowledge. It has shown a 6% improvement in tool-use performance on the DeepSeek-V3.2 benchmark while requiring less than 1% additional experience tokens. This approach addresses the limitations of general-purpose LLMs in geoscience applications.
Key evidence
- RSMeM improves tool-use performance by 6% on the DeepSeek-V3.2 benchmark with less than 1% additional experience tokens.
- The mechanism consists of two components: Hierarchical Knowledge Grounding and Failure-Aware Experience Refinement.
- Extensive experiments on EarthBench demonstrate consistent improvements in tool-use performance across various LLM backbones.
Why it matters
The introduction of RSMeM is significant as it addresses the shortcomings of existing remote sensing agents that rely on general-purpose LLMs, which often lead to unreliable workflows. By incorporating domain-specific knowledge and refining experiences based on failures, RSMeM aims to create more robust and effective tools for geoscience research, potentially leading to more accurate analyses and insights.
Paper Resources
Source Excerpt
Geoscience research requires complex analysis and domain expertise, with remote sensing (RS) observations as a key foundation. However, existing RS agents built on general-purpose remain largely domain-agnostic, resulting in brittle and error-prone workflows. Moreover, these failures are seldom consolidated into a reusable experience for subsequent analyses. To address this issue, we introduce RSMeM, a knowledge-enhanced memory evolution mechanism that bootstraps RS agents with pre-distille
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