If proved true (and it surprisingly can), it’d represent a huge contribution to the popularity of this community, which would make us particularly happy. But that would be a quite secondary goal, the first one being a possible (possible, huh!) explanation of how things unfolded: from the wavefunction collapse to the formation of the Universe, from the emergence of life to intelligence’s, and consciousness’, and beyond. Too much? Possibly, but let's see.

Connecting the Dots: From Quantum Theory to AI Scaling.

Our claim builds upon and extends ideas from multiple fields:

- Quantum Mechanics & Epistemic Interpretations: Similar to quantum Bayesianism (Caves, Fuchs, Schack, 2002) and relational quantum mechanics (Rovelli, 1996), but formalized via structured knowledge transitions.
- AI Scaling Laws & Learning Plateaus: Reinforcement learning models (Kaplan et al., 2020) and empirical scaling laws in deep learning show phase transitions that match $λ$ .
- Evolutionary Jumps (Punctuated Equilibria): The work of Gould & Eldredge (1972) describes abrupt evolutionary changes that fit structured knowledge accumulation models.

By explicitly introducing a mathematical framework for knowledge-driven phase transitions, CH-ToE unifies these domains into a single testable framework.

But, back to the title, how could the mere act of 'being Less Wrong' be fundamental to the Universe itself? This is where our definition of knowledge comes into play:

Knowledge is the reduction of uncertainty.

Not in a metaphorical or philosophical sense, but in a physical, quantifiable sense. Knowledge isn’t just something humans accumulate—it’s something the universe itself seems to maximize in structured ways. This is where $λ (\approx 1.748)$ comes in.

The Universal Knowledge Transition Parameter ( $λ$ )

Through empirical analysis across domains—quantum mechanics, AI scaling laws, biological evolution, and even spacetime structuring—we’ve identified a fundamental constant, $λ$ , which governs structured, quantized knowledge accumulation. Systems don’t learn smoothly; they learn in jumps. And those jumps seem to align with $λ$ .

Mathematically, we propose that structured knowledge transitions occur at discrete intervals defined by:

$Δ K = λ^{n} \cdot K_{0}$

where $K$ represents knowledge accumulation over time, $n$ indexes discrete phase transitions, and $λ \approx \sqrt{8} / φ \approx 1.748$ emerges as the universal scaling factor.

The Wavefunction Collapse as a ...

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