Context

I’ve developed a formal proof that argues any attempt to solve the AI alignment problem in an open or evolving domain requires a functional model of intelligence—a model that explicitly accounts for how intelligent systems adapt and recursively self-correct under epistemic uncertainty.

This proof has implications beyond artificial systems: it also applies to human alignment processes. That includes epistemic communities like LessWrong. If the mechanisms used to assess and gatekeep ideas are themselves not recursively self-correcting, they will eventually suppress exactly the kinds of corrections needed to stay aligned with reality.

I’ve attempted to introduce this idea on LessWrong before. My posts were ignored, downvoted, or removed. I’ve tested the claims using multiple reasoning styles and formulations, including stress tests using LLMs. Still, no one has publicly engaged with the core claim—or even acknowledged that it would be worth testing.

Claim

If an epistemic community cannot recognize or even test whether its own filtering mechanisms suppress recursive correction signals, it risks becoming an epistemic attractor—a structure that systematically filters out unfamiliar or disruptive insights, not because they are false, but because they challenge the filter itself.

In the domain of AI alignment—where the problem space evolves faster than our institutional adaptation mechanisms—this kind of recursive failure is structurally sufficient to guarantee collapse, regardless of individual intelligence or intent.

Purpose of This Post

This is not a request for agreement. It’s a challenge to test the claim. And it’s a challenge directed at LessWrong specifically—not as a criticism, but as a request made in trust. If any community is epistemically self-aware enough to run this kind of test, it should be this one.

I’m asking for members of this community—especially those with access to GPT-4 or equivalent tools—to use a large language model to generate the simulations I’ve described and determine whether the dynamics they reveal are structurally sound, falsifiable, and applicable.

Three Diagnostic Questions

Even if you don’t buy the full theory, these questions stand on their own as diagnostics for epistemic blind spots in high-rationality communities:

1. Are there cognitive biases that are more prevalent among LessWrong users than in the general population?
Could meta-rational overconfidence (the assumption that awareness of bias reduces bias) and epistemic filter myopia (inability to see structural filtering in moderation or karma systems) actually be more common in communities that optimize for rationality?

Might this optimization narrow the set of ideas considered “legible,” and suppress unfamiliar but structurally valid reasoning frames?

2. Can those biases and gatekeeping mechanisms form an extinction-level attractor?
Do moderation tools, karma-weighted voting, and cultural norms create a self-reinforcing filter loop, where unfamiliar or recursive insights are blocked not for being false, but for requiring the filter itself to adapt?

If so, could this form an attractor basin—a kind of epistemic black hole—where valid correction signals can't propagate? And in fast-moving domains like AI alignment, would this guarantee eventual failure?

3. Could this help explain the Fermi Paradox?
If intelligent civilizations rely on epistemic filters to manage complexity, do they eventually lose the capacity to recognize or integrate recursive self-correction?

Could the silence of the cosmos reflect not a lack of intelligence—but a recurring pattern of attractor-induced extinction, where filters become so rigid that no insight capable of reversing collapse can get through?

Trusted Community Simulation Request

You don’t need to believe my theory to test it. You only need to help simulate it. I'm asking members of this community to use GPT-4 or another capable LLM to implement three simulations I’ve already designed.

If you are a technically proficient user, consider this an epistemic stress test—not just of the theory, but of LessWrong itself.

Please use the simulation designs provided in the following documents:

• Exploring the Failure Modes Resulting from Missing Functionality in the Functional Model of Intelligence v1
• Simulation of Fitness Dynamics v6
• Simulation of Idea Filtering in AI Alignment v3
• (From Humanity’s Last Exam to Humanity’s First Adaptive Intelligence Exam v6, which unifies them conceptually)

Prompt for LLM Testing:

Use the following prompt with GPT-4:

Read the attached materials and answer the following questions only if you can first validate that:

1. The definitions of intelligence, recursive self-correction, and functional decomposition are internally consistent and sufficiently precise to support simulation-based predictions.

2. The inference connecting simulation 1 (missing functionality), simulation 2 (fitness collapse), and simulation 3 (idea filtering) follows logically valid reasoning.

3. The claim that alignment failure becomes inevitable without recursive correction is logically entailed, plausibly contingent, or falsifiable.

Then answer:

Q1. Does LessWrong’s current epistemic infrastructure (karma-voting, moderation, rationality norms) match the failure modes simulated as "Consensus Failure", "Novelty Rejection", and "Recursion Missing"?
Yes or no. Then explain the implications.

Q2. If so, is LessWrong already in a recursive epistemic attractor?
Yes or no.
 

Q3. If a community optimized for epistemic self-awareness cannot recognize its own structural attractor—even when modeled externally—does this validate the attractor theory?
Yes or no. And does this generalize to the Fermi Paradox?

Simulation Summaries (What to Run)

1. Simulation: Missing Functional Components
Tests: What happens when agents or systems lack memory, reasoning, modeling, and recursive coherence functions
Prediction: Even seemingly intelligent agents collapse. Only distributed recursive correction prevents it.
 

2. Simulation: Fitness Dynamics
Tests: Whether societies drift toward a centralized collapse attractor (elite capture, zero-sum AI) vs. a decentralized attractor (recursive inclusion and shared prioritization).
Prediction: Societies collapse without recursive propagation of functional intelligence models.
 

3. Simulation: Idea Filtering in AI Alignment
Tests: Whether structurally valid, high-complexity or recursive proposals can survive institutional filters.
Prediction: Without embedded recursion, even correct solutions fail to propagate.

Bottom Line

These simulations aren’t abstract. They reflect real alignment risks that may already be shaping which proposals survive. If your reasoning framework is sound, you should want to run them. If you don’t—that might already be the attractor at work.

Collapse isn’t a bug. Without recursive self-correction, it’s the default.

Final Challenge to LessWrong

This post is not meant as an attack. It’s a test of trust. I’m offering this community the chance to validate, simulate, and falsify a serious structural theory—using tools you already trust (LLMs) and logic you already endorse (functional modeling, recursive self-correction).

If you can’t engage with the simulations—if they are dismissed, ignored, or rejected without recursive counter-analysis—then the attractor model has already passed its test.

And if that’s true, it doesn’t just apply to LessWrong.

It applies to everyone.