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Here’s a paper by Floreano at EPFL from 1997 explicitly on Red Queen dynamics for creating neural networks for intelligent robot control. There was lots of dis

Here’s a paper by Floreano at EPFL from 1997 explicitly on Red Queen dynamics for creating neural networks for intelligent robot control. There was lots of discussion of these ideas in the 1990s. In those days we trained very small NNs - tens of nodes - by evolving their weights and topologies. A run could take days on a workstation of the time. This particular paper is about co-evolving predator and prey, where the behavior of each is the ‘evaluation’ of the other. https://infoscience.epfl.ch/entities/publication/a65d0679-6855-4be6-adf4-6c8288092357 https://infoscience.epfl.ch/entities/publication/a65d0679-68...

jldugger 2mo ago
OP's link: > Now the researchers have addressed this issue by having both the self-improving agent and the evaluator evolve together. and your quote: > This particular paper is about co-evolving predator and prey, where the behavior of each is the ‘evaluation’ of the other. Both sound like the GAN approach that was popularized a decade ago and kinda the start of the "genAI" boom.
- AlexAndreiIacob 2mo ago
  Yes, they apply the exact same principle to different algorithms and domains.
grumbelbart2 2mo ago
Funnily enough, Jürgen Schmidthuber invented / coined the term "Gödel Maschine" in 2003: https://arxiv.org/abs/cs/0309048 https://arxiv.org/abs/cs/0309048 He'll probably have a field day over this.
- AlexAndreiIacob 2mo ago
  For broader context: our work directly builds upon Jürgen Schmidhuber’s Huxley-Gödel Machine, while our research has been cited in a recent survey he co-authored: https://arxiv.org/abs/2607.13104 https://arxiv.org/abs/2607.13104 Anyone interested in this field should definitely engage thoroughly with his body of work.
  - grumbelbart2 2mo ago
    I by no means wanted to suggest the opposite (that's how I found the connection)! Schmidthuber is famous for his groundbreaking work in machine learning, but also for being somewhat left out when people list the "Grandparents of Deep Learning" and for being vocal about that. I just wanted to poke that bit. If your work takes off, it would be a great chance for him to shine.
    - AlexAndreiIacob 2mo ago
      No worries, I just wanted to give proper credit in the comment above. The blog post lacks the broader context and references of the paper, and Schmidhuber and his students deserve recognition. I edited the comment to make this clearer. On this topic, I have been pleasantly surprised to see some of the works he has recently supervised receive direct recognition, particularly the Huxley-Gödel Machine and its many siblings in the field of self-improving AI: https://github.com/metauto-ai https://github.com/metauto-ai
jambalaya8 2mo ago
I remember reading this back then and concluding this was dangerous.

jldugger 2mo ago

OP's link: > Now the researchers have addressed this issue by having both the self-improving agent and the evaluator evolve together. and your quote: > This particular paper is about co-evolving predator and prey, where the behavior of each is the ‘evaluation’ of the other. Both sound like the GAN approach that was popularized a decade ago and kinda the start of the "genAI" boom.

AlexAndreiIacob 2mo ago
Yes, they apply the exact same principle to different algorithms and domains.

grumbelbart2 2mo ago

Funnily enough, Jürgen Schmidthuber invented / coined the term "Gödel Maschine" in 2003: https://arxiv.org/abs/cs/0309048 https://arxiv.org/abs/cs/0309048 He'll probably have a field day over this.

AlexAndreiIacob 2mo ago
For broader context: our work directly builds upon Jürgen Schmidhuber’s Huxley-Gödel Machine, while our research has been cited in a recent survey he co-authored: https://arxiv.org/abs/2607.13104 https://arxiv.org/abs/2607.13104 Anyone interested in this field should definitely engage thoroughly with his body of work.
- grumbelbart2 2mo ago
  I by no means wanted to suggest the opposite (that's how I found the connection)! Schmidthuber is famous for his groundbreaking work in machine learning, but also for being somewhat left out when people list the "Grandparents of Deep Learning" and for being vocal about that. I just wanted to poke that bit. If your work takes off, it would be a great chance for him to shine.
  - AlexAndreiIacob 2mo ago
    No worries, I just wanted to give proper credit in the comment above. The blog post lacks the broader context and references of the paper, and Schmidhuber and his students deserve recognition. I edited the comment to make this clearer. On this topic, I have been pleasantly surprised to see some of the works he has recently supervised receive direct recognition, particularly the Huxley-Gödel Machine and its many siblings in the field of self-improving AI: https://github.com/metauto-ai https://github.com/metauto-ai

jambalaya8 2mo ago

I remember reading this back then and concluding this was dangerous.