RAPTOR-AI for Disaster OODA Loop: Hierarchical Multimodal RAG with Experience-Driven Agentic Decision-Making
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arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| author | Yasuno, Takato |
| author_facet | Yasuno, Takato |
| contents | Humanitarian Assistance and Disaster Relief (HADR) operations demand rapid synthesis of multimodal information for time-critical decision-making under extreme uncertainty. Traditional information systems struggle with the fragmented, multimodal nature of disaster data and lack adaptive reasoning capabilities essential for dynamic emergency contexts. This work introduces RAPTOR-AI, an agentic multimodal Retrieval-Augmented Generation (RAG) framework that advances beyond conventional static knowledge bases by implementing dynamic, experience-driven decision support for disaster response. The system addresses HADR requirements across initial rescue, recovery, and reconstruction phases through three key innovations: hierarchical multimodal knowledge construction from diverse sources (textual reports, aerial imagery, historical documentation), entropy-aware agentic control that dynamically selects optimal retrieval strategies based on situational context, and experiential knowledge integration using LoRA adaptation for both expert and non-expert responders. The framework constructs hierarchical knowledge trees from 46 tsunami-related PDFs (2,378 pages) using BLIP-based image understanding, ColVBERT embeddings, and long-context summarization within the OODA loop (Observe, Orient, Decide, Act) tactical framework. Experiments demonstrate significant improvements over existing approaches: 23\% improvement in retrieval precision, 31\% better situational grounding, and 27\% enhanced task decomposition accuracy, with efficient scaling up to 3,000 document chunks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_00030 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RAPTOR-AI for Disaster OODA Loop: Hierarchical Multimodal RAG with Experience-Driven Agentic Decision-Making Yasuno, Takato Machine Learning I.2.4; I.2.7 Humanitarian Assistance and Disaster Relief (HADR) operations demand rapid synthesis of multimodal information for time-critical decision-making under extreme uncertainty. Traditional information systems struggle with the fragmented, multimodal nature of disaster data and lack adaptive reasoning capabilities essential for dynamic emergency contexts. This work introduces RAPTOR-AI, an agentic multimodal Retrieval-Augmented Generation (RAG) framework that advances beyond conventional static knowledge bases by implementing dynamic, experience-driven decision support for disaster response. The system addresses HADR requirements across initial rescue, recovery, and reconstruction phases through three key innovations: hierarchical multimodal knowledge construction from diverse sources (textual reports, aerial imagery, historical documentation), entropy-aware agentic control that dynamically selects optimal retrieval strategies based on situational context, and experiential knowledge integration using LoRA adaptation for both expert and non-expert responders. The framework constructs hierarchical knowledge trees from 46 tsunami-related PDFs (2,378 pages) using BLIP-based image understanding, ColVBERT embeddings, and long-context summarization within the OODA loop (Observe, Orient, Decide, Act) tactical framework. Experiments demonstrate significant improvements over existing approaches: 23\% improvement in retrieval precision, 31\% better situational grounding, and 27\% enhanced task decomposition accuracy, with efficient scaling up to 3,000 document chunks. |
| title | RAPTOR-AI for Disaster OODA Loop: Hierarchical Multimodal RAG with Experience-Driven Agentic Decision-Making |
| topic | Machine Learning I.2.4; I.2.7 |
| url | https://arxiv.org/abs/2602.00030 |