The paper maps known and new inclusion and separation results for unique hard attention transformer variants into one diagram, with new proofs for a depth hierarchy, separable attention in finite-image models, and masked simulation.
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Comparison of different Unique hard attention transformer models by the formal languages they can recognize
The paper maps known and new inclusion and separation results for unique hard attention transformer variants into one diagram, with new proofs for a depth hierarchy, separable attention in finite-image models, and masked simulation.