How to make an AI research agent label facts vs inferences — a deterministic provenance pipeline

Short summary

To prevent AI agents from presenting inferences as facts, separate the architecture: let the LLM extract and summarize claims, but use deterministic code for all scoring, cross-checking, and labeling. A claim becomes a FACT only when corroborated by ≥2 independent sources or from an official API; everything else is labeled INFERENCE, preventing the model from laundering guesses into facts. This structure guarantees reproducibility—the same query always produces the same labels—and makes every output verifiable per-claim with clear provenance.

• •Separate fact-labeling from LLM extraction; use deterministic rules instead
• •Only label claims as FACT when corroborated by ≥2 independent sources or official APIs
• •Ensures reproducibility and prevents models from promoting inferences as facts

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