DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints

ArXi:2605.27957v1 Announce Type: new 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 than selecting semantically plausible tools: LLMs must generate executable workflows with correct parameter binding and dependency propagation.