Researchers propose 'interpretive debate' as a systematic approach to answering fundamental questions about AI models: Are they scheming? Lying? Introspective? The core problem: you cannot simply ask models these questions and trust their self-reports. The solution: structure the investigation as competing hypotheses—for instance, the model is scheming versus the model is not scheming—then accumulate empirical evidence. Instead of seeking absolute truth, a claim is treated as warranted when it survives adversarial challenge. The methodology rests on three key principles: contrasting hypotheses that enable recursive decomposition, convergence of evidence from independent sources, and applying Occam's razor to data-driven findings. This work, building on prior research in performative misalignment, aims to bring scientific rigor to interpretability questions that have long stymied AI safety research.

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