Language Models’ Hall of Mirrors Problem: Why AI Alignment Requires Peircean Semiosis

Abstract This paper examines some limitations of large language models (LLMs) through the framework of Peircean semiotics. We argue that basic LLMs exist within a “hall of mirrors,” reflecting only the linguistic surface of training data without indexical grounding in a shared external world, and manipulating symbols without participation in socially-mediated epistemology. We then argue that newer developments, including extended context windows, persistent memory, and mediated interactions with reality, are moving towards making newer Artificial Intelligence (AI) systems into genuine Peircean interpretants, and conclude that LLMs may be approaching this goal, and we identify no fundamental architectural barriers that would prevent this. This lens reframes a central challenge for AI alignment: without grounding in the semiotic process, a model’s linguistic encoding of goals may diverge from real-world values. By synthesizing Peirce’s pragmatic view of signs, contemporary discussions of AI alignment, and recent work on relational realism, we illustrate a fundamental epistemological and practical challenge to AI safety and point to part of a solution.