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This paper presents a two-stage LLM pipeline for extracting and triangulating causal evidence from humanitarian crisis reports, achieving strong F1 scores on a ReliefWeb dataset and proposing a Level-of-Evidence score for cross-context convergence.
Discusses the technical method of triangulation by testing network latency to identify and block Chinese users from using AI services (like Claude).