ISHT-SDP: Invariant-Seeded Holonomic Transformers for Semantic Decoherence Protection

Large language models generate fluent text by collapsing a high-dimensional representation onto a next-token distribution. That collapse is computationally convenient and theoretically dangerous. If one treats the residual set of linguistically compatible worlds as an uncollapsed phase space - the space of what language has described only incompletely - then standard Euclidean attention has no protective boundary. The latent geometry thermalizes toward the highest-density regions of the training scrape, and the model asserts facts that are incompatible with the constraints already in context. This paper develops a theoretical architecture, the Invariant-Seeded Holonomic Transformer (ISHT), in which fault tolerance is not obtained from microscopic quantum superposition but from a macro-scale geometric surface code acting on a semantic manifold. Attention is rewritten as a bilinear form with an invariant-seeded metric; residual possibilities are stored as holonomies of a connection rather than as a single simplex of probabilities; and a dissipative checksum loop holds the representation in a nonequilibrium steady state (NESS). We argue that the relevant anti-thermal engine does not have to operate at the scale of tubulin rings or superconducting qubits. It can operate at the „language label”: continuous reconstruction of constraints, pull-back to an invariant seed, and explicit preservation of residual volume. The proposal unifies three strands: (i) the interpretation of consciousness-like uncollapsed potentials as a residual possibility volume bounded by linguistic description; (ii) geometric and topological protection familiar from quantum error correction and Riemannian attention; and (iii) the thermodynamics of driven biological systems that remain ordered only by burning fuel. We state formal objects, architectural modules, training objectives, and falsifiable predictions. A matched-budget micro-simulation on a 256-world checksum language (three-layer transformers, d = 64, two seeds) shows that ISHT is implementable and that all three conditions beat chance on checksum entailment, but it does not show a reliable advantage over Euclidean attention: token NLL is 2.397 for all conditions, contradiction rates are 0.257 (vanilla), 0.272 (metric), and 0.270 (ISHT), and several long-horizon probes saturate on an end-of-sequence policy. The contribution is a theory of semantic NESS, not a claim of empirical supremacy.