Structure and Scale in Simplicial Sequence Modelling
Modern large-scale deep learning exhibits two striking empirical phenomena: behavioural scaling laws (predictable performance gains with increasing scale) and emergent mechanisms (structured internal representations and circuits in deep neural networks). We hypothesise that these two phenomena are connected: that predictable changes in behaviour are the result of predictable changes in internal computational structure. In this paper, we report preliminary evidence of such a connection. We find a correlation between scaling patterns in performance and representations in small transformers trained to predict the outputs of a hidden Markov model, for which residual activations are known to l...
| Authors | Matthew Farrugia-Roberts |
|---|---|
| Primary category | cs.LG |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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