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Logic and the $2$-Simplicial Transformer

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arxiv 1909.00668 v1 pith:ZBTZDYGO submitted 2019-09-02 cs.LG cs.LOstat.ML

James Clift , Dmitry Doryn , Daniel Murfet , James Wallbridge This is my paper
classification cs.LGcs.LOstat.ML
keywords attentiontransformersimplicialarchitecturebiascontextdeepdot-product
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abstract

We introduce the $2$-simplicial Transformer, an extension of the Transformer which includes a form of higher-dimensional attention generalising the dot-product attention, and uses this attention to update entity representations with tensor products of value vectors. We show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning.

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  1. Fast and Simplex: 2-Simplicial Attention in Triton

    cs.LG 2025-07 conditional novelty 6.0 of 10

    2-simplicial attention, implemented in Triton with a sliding window, is claimed to yield a steeper loss-versus-parameters scaling exponent than dot-product attention on math and reasoning benchmarks.

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