The Semiotic-Reflexive Transformer: A Neural Architecture for Detecting and Modulating Meaning Divergence Across Interpretive Communities

Abstract

  Large language models treat meaning as stable. A token maps to a vector, context refines it, and the model proceeds as though semantic content were a fixed quantity transmitted between interlocutors. This assumption is false. Meaning drifts, forks, and collapses across interpretive communities in ways that current architectures cannot detect, let alone represent. This paper introduces the Semiotic-Reflexive Transformer (SRT), a neural architecture that embeds Peircean semiotic decomposition, metapragmatic divergence tracking, and catastrophe-theoretic bifurcation estimation directly into the transformer's computational graph. The architecture decomposes token embeddings into four semiotic subspaces (representamen, object, interpretant, attractor), tracks meaning divergence across community-conditioned representations through a metapragmatic attention mechanism, and estimates bifurcation parameters via a dedicated network that models the cusp catastrophe geometry of meaning collapse. Validation on synthetic data with planted divergence signals confirms that each module learns its intended function: subspace specialization produces interpretable decomposition (linear probing margins ≥ 0.15 across all four tasks), community-conditioned interpretants differentiate contested from neutral terms (3.28× cosine distance ratio), divergence tracking correlates strongly with ground-truth divergence ramps (Spearman ρ = 0.822), and bifurcation detection achieves 100% regime classification accuracy with r̂ differences of 0.659 between pre- and post-bifurcation contexts. The architecture operates in two inference modes: STANDARD (semiotic modules compute but do not modulate output) and REFLEXIVE (bifurcation estimates actively reshape the model's probability distribution over next tokens). The SRT is offered not as a replacement for existing language models but as a proof of concept that semiotic theory, specifically the Peircean framework and its extensions through Silverstein, Derrida, and catastrophe theory, can be operationalized as differentiable neural computation, producing architectures that are aware of the conditions under which their own outputs become unreliable.

Data: 10.5281/zenodo.18876940

Keywords: Semiotic Theory, Transformer Architecture, meaning divergence, Peircean semiotics, bifurcation detection, metapragmatics, interpretive communities, neural architecture, catastrophe theory, computational semiotics