Semantic Laundering in AI Agent Architectures: Why Tool Boundaries Do Not Confer Epistemic Warrant

TL;DR

This paper identifies a systematic architectural flaw in LLM-based AI agents called semantic laundering, which causes unjustified acceptance of information due to tool boundary design, akin to a Gettier problem.

Contribution

It formalizes semantic laundering as an architectural failure, proves the inevitability of self-licensing in such systems, and introduces principles explaining this persistent issue.

Findings

Semantic laundering is a systematic architectural failure.

Self-licensing cannot be eliminated under standard assumptions.

Scaling and improvements do not resolve the core problem.

Abstract

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.