Adversarial Disconfirmation Pipeline
When you ask an LLM to verify its own output, it confirms what it already said. Single-model pipelines share blind spots. Hallucinations are syntactically indistinguishable from accurate outputs — and confidence scores don't help. The only way to surface fabrication is to challenge claims externally with independent evidence.
The SolutionA 6-stage claim verification pipeline grounded in Popperian falsificationism. Extracts claims, subjects each to adversarial disconfirmation under epistemic isolation using a separate model family, verifies citations against external academic databases, reconciles findings with tiered evidence quality, and generates a self-contained analysis report — stress-testing what survives scrutiny rather than confirming what sounds right.
Discuss how DECON can be integrated into your content workflow — whether you're validating AI-generated reports, auditing research, or building trust into published outputs.