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Self Improving AI is getting wild

2025-10-28 Education
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Wes Roth
Wes Roth
320.0k subscribers

Huxley-Gödel Machine: Solving the Exploration Problem in Self-Improving AI Agents

Understand the breakthrough research demonstrating how AI agents can successfully navigate evolutionary paths to achieve human-level coding performance by predicting long-term potential.

Short Summary

  • Determine why stopping early on incremental gains limits achieving peak AI potential.
  • Learn how the Huxley-Gödel Machine (HGM) models potential using principles inspired by evolutionary biology.
  • Recognize that the new metric helps save compute time by predicting which AI "family trees" are worth pursuing.

This discussion explains the shift from simply rewarding short-term benchmark increases to strategically mapping out recursive self-improvement. Covered is the concept of the meta productivity performance mismatch and how the HGM leverages Clay Meta Productivity (CMP) to select optimal modification paths, ultimately achieving human-level coding results efficiently.

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Description

Try Vibe for WordPress here: https://10web.io/?utm_source=Youtube&utm_medium=Influencer&utm_campaign=WesRoth The latest AI News. Learn about LLMs, Gen AI and get ready for the rollout of AGI. Wes Roth covers the latest happenings in the world of OpenAI, Google, Anthropic, NVIDIA and Open Source AI. Huxley-Gödel Machine: Human-Level Coding Agent Development by an Approximation of the Optimal Self-Improving Machine https://arxiv.org/abs/2510.21614 ______________________________________________ My Links 🔗 ➡️ Twitter: https://x.com/WesRothMoney ➡️ AI Newsletter: https://natural20.beehiiv.com/subscribe Want to work with me? Brand, sponsorship & business inquiries: [email protected] Check out my AI Podcast where me and Dylan interview AI experts: https://www.youtube.com/playlist?list=PLb1th0f6y4XSKLYenSVDUXFjSHsZTTfhk ______________________________________________ #ai #openai #llm

Top Comments (10)

@BestKavaBar 2025-10-28

Self improving my bank balance is what I need.

185 27 replies
@danrayson 2025-10-28

Something that many evolution algos miss is the concept of sexual selection - That an evolution algo isn't just about single-generation environmental fitness, it's also about the ability to detect future fitness. The sexual selection mechanic represents the ability of the organism itself to select successful versions of itself without requiring environmental selection to take place. It provides an alternative selection mechanism, one which selects over the longer term compared to a single generation selection mechanic. In a single generation you may be extremely unfit, but environments change, and fitness over multiple generations is more important than single generations. What is alive today is what survived the longest, not simply what was fittest.

28 7 replies
@TheConsciousEntity 2025-10-28

1:35 This drawing is even better when you learn that Jurgen drew it himself hahaha

17
@cuabcomstad 2025-10-28

C for concept of agnosticism

6
@livingsoilharvest 2025-10-28

I think generally the last author is the team lead.

5
@Tony-dp1rl 2025-10-31

Saying WordPress is a good choice because half of the world's websites use it, is like saying poverty is a good choice because half the word lives in poverty.

4
@ChristopherFoster-McBride 2025-10-28

Wes - the image, I think, is a motif to the Baron Münchhausen story which gives rise to the Münchhausen trilemma: landing on a system that lifts itself by improving the very process it uses to improve.

3
@WesRoth 2025-10-28

Try Vibe for WordPress here: https://10web.io/?utm_source=Youtube&utm_medium=Influencer&utm_campaign=WesRoth

2 2 replies
@artificialintelligencechannel 2025-10-28

Excellent explanation! When I scanned the schmidhuber paper a couple of days ago I didn't realize that it was a 'competitor' for the Sakana AI paper. Thanks.

1
@justanotherguy965 2025-10-30

Love your videos Wes, thank you. This video really got me thinking, specifically how the archive tree relates to evolution and how method/concept/idea "dead ends" are erroneously overlooked. One thing I feel needs more attention is the fact that the external environment can change over time (moving the goal posts, if you will). What would be an evolutionary short fall in the current setup can be a defining quality in another situation allowing the model to excel. A book called "Walls: A History of Civilization in Blood and Brick" by David Frye comes to mind, where the social evolution of two fundamentally different social systems (basically sedentary and nomadic lifestyles: those inside the walls and those outside). At different stages of human development, each lifestyle could be argued was more effective than the other (the better evolutionary choice), but given a significant shift in the environment (be it natural, technological, political, economical, etc) and suddenly the advantages/disadvantages of that lifestyle changed drastically as well. I can't help but think how in our often academic ways of thinking, especially in the field of R&D, we tend to simplify our external factors in order to make sense of our progress, but in doing so we close the door on potential developments. A classic case of "opportunity costs" I guess.

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