A “diff” tool for AI: Finding behavioral differences in new models

Introduced the Dedicated Feature Crosscoder (DFC), a novel tool for comparing AI models with different architectures. The DFC utilizes a three-section dictionary (shared, language-specific) to identify unique features and behavioral differences, moving beyond traditional model diffing limitations. Demonstrated its application by identifying and validating features like 'Chinese Communist Party Alignment' and 'American Exceptionalism' in open-source models, and a 'Copyright Refusal Mechanism' in GPT-OSS-20B. This enables a more proactive and comprehensive approach to AI safety auditing by flagging 'unknown unknowns' and facilitating targeted behavioral analysis through feature suppression and amplification.