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The article explores whether AI agents operating independently online will require separate identity frameworks for humans, organizations, and AI, with different rules and trust models, questioning if this separation is necessary.
This paper introduces RealityTest, a multimodal, multilingual benchmark to evaluate whether AI systems disclose their identity when probed by users, based on real human queries collected across 49 countries. It finds that only 31% of people ask directly about identity, and that human questions are more diverse than synthetic ones, revealing that phrasing and context matter more for disclosure than the specific model.
A research report detailing controlled experiments on building an external memory architecture that enables persistent AI identity independent of model weights, finding that accumulated fragment history consistently dominates system prompts in shaping output across three topologies.