mlsanityData to 7 Oct 2026

An Agentic Interface for End-to-End Probabilistic Seismic Hazard and Risk Analysis

arXiv 2607.16249 ↗First posted 27 Jun 2026physics.geo-ph cs.AIcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

Probabilistic seismic hazard and risk analyses are backbone to building codes, insurance pricing, and disaster management. Yet their open-engine pipelines remain accessible primarily to experts. We present the first agentic interface to the end-to-end probabilistic seismic hazard and risk chain via an open-source server, addressable through the Model Context Protocol (MCP). MCP wraps the OpenQuake engine and the 2020 European Seismic Hazard and Risk Models using twenty-four typed endpoints. Here, an agent is defined as a large language model (LLM) with tools. LLM is confined to the role of an orchestrator so it plans, translates, and explains, while the OpenQuake engine and custom codes compute hazard values, damage probability, and loss. Each response carries source-model, ground-motion model (GMM), and certified data provenance for transparency. Results are benchmarked against ESHM20 at seventy-three cities, the replicated 475-year spectral accelerations match official values within a median of 5 %, and a full hazard-to-loss estimate runs in minutes. The interface additionally accepts a user-supplied empirical or machine-learning GMM on any tectonic region type of the published tree, and adds additional features that existing web services omit: conditional spectra, deterministic scenarios, surface hazard, per-building loss, retrofit comparison, and record selection with waveform retrieval. The proposed design layers transfer to other regional models, and to other hazards.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 17 of its 55 references that are in the map. First posted on arXiv 27 Jun 2026.

Builds on

17 of 55 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025CLIMATEAGENT: Multi-Agent Orchestration for Complex Climate Data Science Workflows25 Nov
  2. 2025Automating Structural Engineering Workflows with Large Language Model Agents13 Oct
  3. 2025A Lightweight Large Language Model-Based Multi-Agent System for 2D Frame Structural Analysis6 Oct
  4. 2025Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents27 Sep
  5. 2025AQUAH: Automatic Quantification and Unified Agent in Hydrology4 Aug
  6. 2025Accelerating Earth Science Discovery via Multi-Agent LLM Systems7 Mar
  7. 2024An LLM Agent for Automatic Geospatial Data Analysis24 Oct

Other references · 10

  1. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
  2. 2025Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directionscited by 74
  3. 2025LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directionscited by 49
  4. 2025Securing the Model Context Protocol (MCP): Risks, Controls, and Governancecited by 22
  5. 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptionscited by 15
  6. 2024Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamicscited by 8
  7. 2025LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessmentcited by 6
  8. 2025Experiences with Model Context Protocol Servers for Science and High Performance Computingcited by 6
  9. 2026From Research Question to Scientific Workflow: Leveraging Agentic AI for Science Automationcited by 3
  10. 2025BioinfoMCP: A Unified Platform Enabling MCP Interfaces in Agentic Bioinformaticscited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Seismology modeling agent: A smart assistant for geophysical researchers
  2. 2026TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology
  3. 2026Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching
  4. 2026HazardWeaver: Scientific Route Selection for Hazard Analysis Agents
  5. 2026Automating structural reliability analysis with a multi-agent large language model framework
  6. 2026Consensus-gated Multi-Agent Neural Architecture Search for Seismic Fault Segmentation
  7. 2025A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation
  8. 2026Empirical Grounding Improves the Realism of LLM Agents Simulating Human Behavior During Disruptions