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 $\lambda$.
- 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 $\lambda (\approx 1.748)$ comes in.

The Universal Knowledge Transition Parameter ($\lambda$)

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

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

$$\Delta K={\lambda }^n\cdot K_0$$

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

The Wavefunction Collapse as a Knowledge Event.

In standard quantum mechanics, the wavefunction collapse is treated as a mathematical formality—something that just happens upon measurement. But what if it’s more than that? What if the collapse isn’t just a loss of superposition but rather a fundamental knowledge event—an irreversible gain of structured information that follows the same $\lambda$-driven rules as learning in AI or biological evolution?

In other words, we propose that:

> Wavefunction collapse isn’t just a random event—it’s a structured, quantized transition in the universe’s knowledge state.

Mathematically, we model this as:

$$S_f-S_i={\lambda }^m\cdot \Delta S_{min}$$

where $S$ represents system entropy, $\Delta S_{min}$ is the minimum possible entropy shift upon observation, and $m$ indexes quantized epistemic phase transitions.

Scaling Up: Why This Matters Beyond Quantum Mechanics

If wavefunction collapse is driven by structured knowledge accumulation, then this same principle should apply at every scale:

- AI Learning: Machine learning doesn’t progress linearly; it has discrete learning plateaus. These plateaus, surprisingly, align with $\lambda$.
- Biological Evolution: Punctuated equilibria (sudden evolutionary jumps) follow the same structured knowledge transitions.
- Cosmology: The universe itself undergoes discrete jumps in complexity, from early inflation to galaxy formation to intelligence emergence.

In AI scaling, empirical tests suggest that performance gains follow:

$$L\left(t\right)=L_0+A\cdot {\lambda }^p$$

where $L\left(t\right)$ represents cumulative learning, $p$ indexes significant breakthroughs, and $A$ is a normalization factor. If CH-ToE is correct, this pattern should universally manifest across different learning paradigms, from deep learning to biological intelligence.

The Challenge: Falsify This

This isn’t just speculation. CH-ToE makes clear, testable predictions.

- If wavefunction collapse is purely stochastic, why do we observe structured transitions?
- If AI scaling laws are purely computational, why do they match $\lambda$-driven plateaus?
- If evolution is gradual, why do we see knowledge phase transitions in genetic complexity?

Either these are coincidences, or they point to something deeper.

Final Thoughts: Let’s Find Out

We recognize that CH-ToE makes bold claims, and we expect scrutiny. The challenge to the LessWrong community is simple: does the data support this theory, or can it be decisively falsified?

If you want to take a look at the preprint (the full paper will be released shortly), here is the link:  
(https://drive.google.com/file/d/12m5zQZFYp-nB2c3nEJ6QuHfI_CUpzaQ3/view?usp=drive_link).

Challenge it, test it, and prove it wrong. What better way for us to become less wrong?

If it disagrees with experiment, it’s wrong.” — Richard Feynman