aiqbench analysis
This study reveals that decoder-only large language models fundamentally collapse reading and writing into a single entangled mechanism, contrasting sharply with the human brain's ability to dissociate these functions neurologically. By quantifying this coupling across multiple model architectures, researchers establish LLMs as distinct computational entities rather than biological analogues, highlighting unique structural constraints in current AI systems.
Reading Without a Reader: Large Language Models Collapse Reading and Writing into a Single Entangled Code
arXiv:2607.24797v1 Announce Type: cross Abstract: 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, $\rho=0.00$). 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.
Abstract: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, $\rho=0.00$). 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.
Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2607.24797 [q-bio.NC]
(or arXiv:2607.24797v1 [q-bio.NC] for this version)
https://doi.org/10.48550/arXiv.2607.24797 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 353 words
