RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation
RSMeM introduces a knowledge-enhanced memory evolution mechanism for remote sensing (RS) agents, combining hierarchical knowledge grounding and failure-aware experience refinement. Hierarchical Knowledge Grounding uses taxonomy-aware retrieval over a domain corpus to guide planning and tool selection in RS tasks. Failure-Aware Experience Refinement distills failure-annotated tool-use traces into reusable constraints, enabling iterative improvement of multi-step execution. Experiments on EarthBen
Analysis
TL;DR
- RSMeM introduces a knowledge-enhanced memory evolution mechanism for remote sensing (RS) agents, combining hierarchical knowledge grounding and failure-aware experience refinement.
- Hierarchical Knowledge Grounding uses taxonomy-aware retrieval over a domain corpus to guide planning and tool selection in RS tasks.
- Failure-Aware Experience Refinement distills failure-annotated tool-use traces into reusable constraints, enabling iterative improvement of multi-step execution.
- Experiments on EarthBench show consistent performance gains across LLM backbones, with a 6% accuracy boost on DeepSeek-V3.2 using minimal additional tokens.
Why It Matters
This work addresses a critical gap in domain-specific AI agents: the lack of reusable, distilled experience from failures in complex workflows like geoscience analysis. By integrating pre-distilled knowledge with iterative learning from errors, RSMeM offers a blueprint for building more robust, adaptable agents in specialized fields where general-purpose LLMs fall short.
Technical Details
- Hierarchical Knowledge Grounding: Employs a taxonomy-aware retrieval system over a structured domain corpus to inform task planning and tool selection, ensuring alignment with remote sensing best practices.
- Failure-Aware Experience Refinement: Captures and annotates failed tool-use sequences during execution, converting them into reusable constraints that refine future attempts without requiring full retraining.
- Iterative Evolution Mechanism: Alternates between knowledge grounding and experience refinement, allowing the agent to progressively absorb task-level domain knowledge and translate it into instance-level execution improvements.
- Evaluation Framework: Tested on EarthBench, a benchmark for remote sensing tasks, demonstrating consistent improvements across diverse LLM backbones (e.g., DeepSeek-V3.2).
- Efficiency: Achieves significant accuracy gains (6%) with minimal overhead (<1% additional tokens), highlighting the high knowledge density of the distilled experience.
Industry Insight
- The approach underscores the value of embedding domain-specific knowledge directly into agent workflows rather than relying solely on general-purpose models, a trend likely to grow in specialized industries like geoscience, healthcare, or finance.
- Iterative learning from failures could become a standard practice for deploying reliable AI systems in high-stakes environments where errors are costly and hard to predict.
- The success of RSMeM suggests that lightweight, modular enhancements (e.g., memory mechanisms) can yield substantial performance gains without requiring massive model retraining, making it accessible for resource-constrained teams.
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