agent/papers/items/2026-2605-11633-can-llm-agents-respond-to-disasters-benchmarking-heterogeneous-geospatial-reason.md

Paper: Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations

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type: paper title: Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations authors: Junjue Wang, Weihao Xuan, Heli Qi, Pengyu Dai, Kunyi Liu, Hongruixuan Chen, Zhuo Zheng, Junshi Xia, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2605.11633 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-12 updated_at: 2026-05-12 status: queued relevance: high topics:

• agent-evaluation
• planning
• rag
• reasoning
• tool-use
• workflow-agent methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

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related_projects:

collection_score: 21 collection_queries: planning-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: planning-agent
• inferred topics: agent-evaluation, planning, rag, reasoning, tool-use, workflow-agent
• arXiv categories: cs.AI
• collection score: 21

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2605.11633