From Digital Disease Templates to Language-Mediated Belief/Behavior Attractors: A Derivative-to-Primitive Analysis of AI Mind-Virus Phenomena

Abstract

We examine the claim that 'mind viruses' generated by large language models can become biological pathogens. The primitive assertion fails: a digital information pattern cannot transmute into a biological agent. The derivative phenomenon is real and operationally important: language-mediated belief/behavior attractors can propagate through human-agent conversation, reinforce themselves via model sycophancy and anthropomorphic framing, and produce downstream harm. We model this mechanism as a contagion of attractors and describe a defensive architecture (EPHEMERIS/Warden) with a shared epistemic floor (ladder corpus). We also introduce an Abstract Language Model with a Beer-Lambert defuzzification gate as a novel abstraction firewall.