# Dissociating model architectures from inference computations

arXiv: https://arxiv.org/abs/2507.15776 · HTML: https://mlsanity.com/paper/2507.15776/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Key-value memory in the brain](https://mlsanity.com/paper/2501.02950/) (2025)
- [Hierarchical generative modelling for autonomous robots](https://mlsanity.com/paper/2308.07775/) (2023)
- [Linking fast and slow: the case for generative models](https://mlsanity.com/paper/2308.10618/) (2023)

## Abstract (arXiv)

Parr et al., 2025 examines how auto-regressive and deep temporal models differ in their treatment of non-Markovian sequence modelling. Building on this, we highlight the need for dissociating model architectures, i.e., how the predictive distribution factorises, from the computations invoked at inference. We demonstrate that deep temporal computations are mimicked by autoregressive models by structuring context access during iterative inference. Using a transformer trained on next-token prediction, we show that inducing hierarchical temporal factorisation during iterative inference maintains predictive capacity while instantiating fewer computations. This emphasises that processes for constructing and refining predictions are not necessarily bound to their underlying model architectures.