LLM-as-an-Investigator: Evidence-First Agentic Reasoning for Robust Interactive Problem Diagnosis

arXiv:2606.13220v2 Announce Type: replace Abstract: Large language models (LLMs) are used for technical troubleshooting, where problem descriptions are often incomplete and multiple causes can explain the same symptoms. Effective diagnosis requires acquiring discriminative evidence and selecting the explanation best supported by it. Yet reasoning or interaction alone may not prevent premature commitment to an incorrect hypothesis. We introduce LLM-as-an-Investigator, an evidence-first methodology for interactive problem diagnosis that reduces premature commitment while distinguishing competing

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