Reading Without a Reader: Large Language Models Collapse Reading and Writing into a Single Entangled Code

In the literate human brain, reading and writing are two doubly-dissociable systems: a ventral decoding route (impaired in pure alexia) and a fronto-parietal encoding route (impaired in pure agraphia), sharing a partial orthographic core. A decoder-only large language model (LLM) instead drives both from a single autoregressive path optimized on text, a recent cultural invention rather than an evolved instinct. We ask how entangled that one mechanism is, comparing an input-side "reading code" W_E with an output-side "writing code" W_U via an entanglement index E \in [0,1] (CKA, Procrustes residual, mutual k-NN) calibrated against an independent-init floor and a tied ceiling. Across nine probes on GPT-2, OPT, Pythia (14M--1.4B), T5, and BERT/RoBERTa (six consolidating established results, three introducing the read/write analysis), two complementary levels agree in direction. In the weights, untied models hold one coupled but sub-ceiling code (E=0.23--0.35, far above floor) on a non-monotonic couple-then-differentiate trajectory, with W_U drifting \sim 3.2\times farther than W_E in every frequency decile. In behaviour, comprehension and production are positively coupled in all 12 non-degenerate models (sign test p<0.001), the opposite of the brain's double dissociation. This coupling is general, not decoder-only: encoder--decoders separate the two pathways representationally (up to 0.96) yet stay behaviourally coupled. We report our nulls plainly (the geometry \rightarrow behaviour bridge is null, ρ=0.00). Because a single forward path makes some coupling expected a priori, our contribution is its quantification and cross-level concordance; by analogy, not homology, this situates LLMs as a distinct point in the space of possible minds.