DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints

• URL: http://arxiv.org/abs/2605.27957v1
• Date: Wed, 27 May 2026 04:50:23 GMT
• Title: DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints
• Abstract summary: Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools.<n>Effective coordination requires more than selecting semantically plausible tools.<n>We introduce DisasterBench, a benchmark for evaluating structured multi-agent planning.
• Score: 29.573427850164222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools, from satellite analysis to flood prediction and damage assessment, into coherent multi-step workflows. As LLMs increasingly serve as orchestrators of such pipelines, effective coordination requires more than selecting semantically plausible tools: LLMs must generate executable workflows with correct parameter binding and dependency propagation. We introduce DisasterBench, a benchmark for evaluating structured multi-agent planning over semantically similar but operationally distinct disaster-response tools. To enable step-level failure attribution, we further propose First-Point-of-Failure (FPoF), which localizes the earliest root cause in a predicted workflow, separating primary errors from downstream cascading effects. Our evaluation reveals three findings: planning method effectiveness depends strongly on model capacity; tool mismatch and parameter-binding errors dominate first failures, revealing semantic grounding and execution consistency as distinct bottlenecks; and verbose intermediate reasoning can create instruction clash with structured output requirements, disrupting plan generation. Together, these findings highlight a fundamental gap between semantic reasoning and execution-grounded coordination, underscoring the need for planning frameworks that jointly model semantic intent, execution constraints, and workflow consistency. Code, data, and evaluation resources are available at: https://github.com/TamuChen18/DisasterBench_Open

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