Gradland: On Phenomenal Experience, Differentiated Across Many Dimensions
Quick summary
arXiv:2609.09306v1 Announce Type: new Abstract: This paper investigates the hypothesis that the first-order structure of physical interactions, i.e. gradients or Jacobians, characterizes the structure of phenomenal experience. It does so in an idealized world inhabited by neural networks, Gradland, where the physics are known and the functions are (mostly) differentiable. The paper introduces two measures of Jacobian structure: effective rank and cohesion, based on Kirchhoff complexity. Applying the measures to a series of worked examples shows the hypothesis accounts for: (1) the duration of
Key takeaways
- arXiv:2609.09306v1 Announce Type: new Abstract: This paper investigates the hypothesis that the first-order structure of physical interactions, i.e.
- gradients or Jacobians, characterizes the structure of phenomenal experience.
- It does so in an idealized world inhabited by neural networks, Gradland, where the physics are known and the functions are (mostly) differentiable.
Why it matters
“Gradland: On Phenomenal Experience, Differentiated Across Many Dimensions” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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