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This paper introduces a unified benchmark for span-level hallucination detection in RAG systems that extends beyond natural language to code, tool output, and structured documents, and presents a fine-tuned Qwen3.5-2B detector that outperforms existing methods on these new domains while remaining competitive on standard NLP benchmarks.