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Design and Optimization Strategies of a High-Performance Vented Box
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Closing the box.

Thomas Parr1, Giovanni Pezzulo2, Karl Friston3

  • 1Nuffield Department of Clinical Neurosciences, University of Oxford, UK.

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|July 31, 2025
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Summary
This summary is machine-generated.

This discussion explores internal models in AI and brains, addressing how their formulation and degeneracy are resolved. Key themes include model dimensions, non-Markovian time, and collective linguistic communication.

Keywords:
Memoryattentionlanguagepredictiontransformers

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Neuroscience

Background:

  • Commentaries on a prior discussion article provided valuable insights.
  • Several themes emerged from the discussions, highlighting areas for further exploration.

Purpose of the Study:

  • To address common themes raised in commentaries on a discussion article.
  • To delve deeper into the formulation and resolution of degeneracy in internal models.
  • To explore related concepts such as model dimensions, non-Markovian time, and collective communication.

Main Methods:

  • Analysis of commentaries on a previous discussion article.
  • Synthesis of recurring themes and insights from the commentaries.
  • Elaboration on specific topics including internal model degeneracy, model architecture, and temporal dynamics.

Main Results:

  • Degeneracy in internal model formulation and its resolution was a primary focus.
  • The role of model width versus depth was identified as a significant consideration.
  • Non-Markovian time phenomenology and the multi-agent nature of linguistic communication were also discussed.

Conclusions:

  • Internal models, whether in AI or brains, exhibit degeneracy that requires resolution.
  • Model architecture (width vs. depth) and temporal dynamics (non-Markovian time) are crucial.
  • Linguistic communication is inherently a collective, multi-agent endeavor.