The author has developed Epistemic Shield, an experimental framework that treats an LLM's initial response as a provisional hypothesis rather than final output. The system decomposes answers into atomic claims and subjects them to adversarial self-critique to measure consistency across stochastic evaluations.
- Decomposes responses into atomic claims for granular analysis.
- Subjects each claim to adversarial self-critique.
- Measures consistency across multiple stochastic evaluations.
- Incorporates external evidence when available.
- Applies asymmetric penalties to unsupported claims in high-risk domains.
- Allows partial abstention rather than forcing the model to answer everything.
The author is seeking technical criticism, benchmark suggestions, and collaborators to test whether this architecture improves calibration or hallucination resistance.