Agentic Hazard Mapper is a multi‑agent system that automates the creation of hazard maps by emulating professional cartographic workflows and design trade‑offs. The system adapts its design decisions to varying data, locations, and hazard contexts, and was evaluated through a formative study with map experts and a user study with hazard practitioners. Results showed that the generated maps improved spatial understanding and evacuation‑planning performance, reduced perceived workload and task completion time, and were generally preferred over municipal maps.
arXiv:2609.00046v1 Announce Type: cross Abstract: Rapid and reliable disaster mapping of impacted areas, damaged infrastructure, and affected populations is essential for emergency response and recov...
arXiv:2606. 09848v1 Announce Type: cross Abstract: As generative and agentic AI becomes embedded in everyday products, practitioners face a persistent challenge: how to design human-AI coordination -- the ongoing mutual adjustment between users and AI systems as mediate through interfaces-that supports usability, trust, and safety.
Effective crisis response requires spatially grounded communication that bridges linguistic guidance of civilians with the physical environment, accounting for structural bottlenecks, evolving threats, and agent-specific contexts. Yet, current NLP research in crisis communication remains mainly limited to static, text-only classification settings, overlooking the critical communicative role of AI operators in dynamic, embodied scenarios.
The paper introduces ANASSA, an agentic AI orchestration framework designed for spatial intelligence in geographic information systems. It addresses gaps in current systems by integrating structured spatial reasoning, multi‑agent workflow orchestration, execution feedback, authoritative validation, provenance, uncertainty handling, and human decision authority. The architecture is detailed with eleven components across four layers, a six‑step Geospatial AI Cognitive Loop, cross‑component contracts, and governance mechanisms to ensure traceability, reproducibility, and accountability.
arXiv:2606. 31038v1 Announce Type: cross Abstract: For virtual humans to appear believable, they must exhibit agency and spatial awareness while interacting with their environment in ways that reflect competence and intelligence.
arXiv:2610.34569v1 Announce Type: new Abstract: Industrial spatial decisions are assembled across systems that share neither a reference frame nor an owner, and analysts spend substantial effort reco...
arXiv:2511. 15830v2 Announce Type: replace Abstract: Despite rapid progress in artificial intelligence, current systems struggle with the interconnected challenges that define real-world decision making.
MAPF-World introduces an autoregressive action world model for multi‑agent path finding that predicts short‑horizon local observations and neighboring agents’ action intentions, enabling decisions beyond immediate local views. The model incorporates a spatio‑agent positional encoding within Transformer architectures to enhance spatial awareness and agent semantics, fostering coordinated behaviors. Experiments on diverse maps, including a new automated map generator based on real‑world urban layouts, show that MAPF‑World outperforms existing learned solvers, achieving strong performance, robust zero‑shot generalization, and high success rates even at high agent densities.
arXiv:2607. 00989v1 Announce Type: cross Abstract: Semantic trajectory analysis has recently emerged as an approach for modeling human movement by capturing implicit patterns and behaviors through semantic information (e.
Urban diagnosis integrates heterogeneous observations to identify urban problems, localize affected areas, and investigate contributing factors, informing evidence-based urban planning and management....
Mine Odyssey is a new benchmark that tests agentic spatial intelligence by having agents navigate Minecraft reconstructions of real-world locations. The benchmark includes 180 tasks across 30 sites in 20 countries, featuring 20 outdoor and 10 indoor settings with varied scales, layouts, and terrains. Each task gives a natural‑language instruction to visit specific waypoints in order, requiring agents to find routes, open doors, and manage navigation errors.
arXiv:2606. 26614v1 Announce Type: cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis).