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Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Dissociating model architectures from inference computations.

Noor Sajid1,2, Johan Medrano3,4,5

  • 1Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, USA.

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|July 17, 2025
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Summary
This summary is machine-generated.

This study reveals that autoregressive models can mimic deep temporal models by structuring context access. This finding suggests prediction processes are not strictly tied to specific model architectures, optimizing computational efficiency.

Keywords:
Deep temporal structureslanguage modelsstructured context accesstransformers

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Non-Markovian sequence modeling presents challenges for traditional autoregressive and deep temporal models.
  • Existing research often conflates model architecture with inference computation.

Purpose of the Study:

  • To investigate the differences between auto-regressive and deep temporal models in non-Markovian sequence modeling.
  • To dissociate model architecture from inference computations.
  • To demonstrate how autoregressive models can emulate deep temporal computations.

Main Methods:

  • Utilized a transformer model trained on next-token prediction.
  • Implemented iterative inference to structure context access.
  • Induced hierarchical temporal factorization during inference.

Main Results:

  • Autoregressive models successfully mimicked deep temporal computations through structured context access.
  • Hierarchical temporal factorization maintained predictive capacity with reduced computations.
  • Demonstrated that prediction construction is independent of underlying model architecture.

Conclusions:

  • Model architecture and inference computation can be decoupled.
  • Autoregressive models can effectively perform complex temporal modeling tasks.
  • Optimized computational efficiency in sequence modeling is achievable by separating prediction processes from architecture.