@inproceedings{wu-etal-2026-rsmem,
    title = "{RSM}e{M}: Knowledge-Enhanced Memory Evolution for Remote {S}ensing Agents with Systematic Evaluation",
    author = "Wu, Bingxian  and
      Zhang, Yu  and
      Guo, Zonghao  and
      Liu, Tang  and
      Qian, Chen  and
      Lu, Yuxiang  and
      Du, Xingbo  and
      Li, Yanghao  and
      Zhang, Yidan  and
      Chen, Chi  and
      Yao, Ling  and
      Zhou, Chenghu  and
      Sun, Maosong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1519/",
    pages = "32899--32915",
    ISBN = "979-8-89176-390-6",
    abstract = "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 LLMs 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-distilled domain knowledge and iteratively integrates online experience for robust multi-step tool execution. RSMeM is composed of two components: (i) Hierarchical Knowledge Grounding, which performs taxonomy-aware retrieval over a hierarchical domain corpus to guide planning and tool selection; and (ii) Failure-Aware Experience Refinement, which distills failure-annotated tool-use traces into reusable constraints for next-round tool execution. By iteratively employing these two processes, RS agents can evolve to absorb task-level domain knowledge and effectively translate it into instance-level execution experience. Extensive experiments on EarthBench demonstrate that RSMeM consistently improves tool-use performance and end-to-end accuracy across a diverse set of LLM backbones. Notably, RSMeM achieves a 6{\%} accuracy improvement on DeepSeek-V3.2 with less than 1{\%} additional experience tokens, demonstrating the knowledge density of our distilled experience. All codes and models will be released to support reproducible research."
}