Claim Verification

DECON

Adversarial Disconfirmation Pipeline

An LLM asked to verify itself will confirm what it already said.

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.

A 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.

Status

Production · Light Engine deployed

Category

Claim Verification

Deployment

AWS · Serverless (Lambda Durable Functions)

Interested in DECON?

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.

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