Semantic Laundering in AI Agent Architectures: Why Tool Boundaries Do Not Confer Epistemic Warrant
This addresses a foundational problem in AI safety and epistemology for developers of LLM-based agent systems, revealing a systematic architectural vulnerability rather than an incremental improvement.
The paper identifies semantic laundering as a systematic architectural flaw in LLM-based agent architectures where propositions gain unwarranted epistemic status by crossing trusted interfaces, formalizing this as an architectural realization of the Gettier problem. It proves the Theorem of Inevitable Self-Licensing, showing that circular epistemic justification cannot be eliminated under standard assumptions, and demonstrates that scaling, model improvement, and LLM-as-judge schemes cannot fix this type-level issue.
LLM-based agent architectures systematically conflate information transport mechanisms with epistemic justification mechanisms. We formalize this class of architectural failures as semantic laundering: a pattern where propositions with absent or weak warrant are accepted by the system as admissible by crossing architecturally trusted interfaces. We show that semantic laundering constitutes an architectural realization of the Gettier problem: propositions acquire high epistemic status without a connection between their justification and what makes them true. Unlike classical Gettier cases, this effect is not accidental; it is architecturally determined and systematically reproducible. The central result is the Theorem of Inevitable Self-Licensing: under standard architectural assumptions, circular epistemic justification cannot be eliminated. We introduce the Warrant Erosion Principle as the fundamental explanation for this effect and show that scaling, model improvement, and LLM-as-judge schemes are structurally incapable of eliminating a problem that exists at the type level.