100kb physics alignment Simulation running:

https://youtu.be/Gp7a-fXcRNM?si=zp7vqQEU34yGmk2B

H(x) or The Sundog Alignment Theorem proposes that robust alignment can emerge from agents interacting with structured environments via indirect signals—specifically, shadow convergence and torque feedback—rather than direct reward targeting or instruction.

Inspired by atmospheric sundogs (light halos visible only at indirect angles), we construct a simulated system where an agent aligns a mirrored pole to a plumb laser beam, not by observation of the goal, but by detecting torque resistance and the convergence of shadow "bloom" on a structured ceiling grid.

I've spent ten years learning to insert screws to a ceiling using an invisble laser mark and weeks ago i had to train some ESL guys how to align with with these shadow physics. Here is how we turned that into an AI alignment experiment.

I’m a blue collar regular drop-out and independent researcher, previously an electrician, now an automation engineer. I submit plans for $100m dollar computer builds that my customers love but I'm apparently too illiterate to communicate with people who moderate the internet since this program is too naughty and getting me banned from everywhere I try to publish. 

I’ve spent a decade building infrastructure—automation, quantum enclosures, high-torque mounts for server racks, laser alignment systems—and in that time, I developed a deep muscle memory for torque. Specifically, how the feel of a pole twisting against your hand could tell you more than an equation ever could.

I realized something during ceiling installs:

When I'm pushing a fastener into the ceiling with an 18-foot pole, I can't see the tip. I aim a laser plumb line, then rotate the base of the pole until the bloom of reflected light tightens into a singularity. Halo collapse.

I'm aligning to a shadow structure using indirect feedback — shadow, torque, reflection.

So I asked: What if an agent could align like this? And that's where the trouble began

~The Experiment~

I built a simulation in MuJoCo at the public library:

A jointed pole with a mirrored tip.

A laser beam projected from the floor to a ceiling.

A ceiling untextures, then intuitively textured with honeycom  fields: golden spirals, harmonic waves, hurricane geometries.

My agent never sees the goal.

It only feels torque at the joints and watches how its shadow blooms against the ceiling.

The goal?

Learn to align—not by seeing—but by feeling resonance with the structure.

The Theorem

I formalized the concept as:

> H(x) = ∂S / ∂τ

Where:

S is the shadow projection field,

τ is torque at the joints,

H(x) is the “halo signature”.

If H(x) ≠ 0, we say alignment has emerged.

Not because the agent was told what to do, but because it inferred structure through interaction.

I call it:

The Sundog Alignment Theorem — named after the atmospheric phenomenon that only appears at indirect angles.

The Agents

We ran three:

DOA — Direct Observation Agent (reward-trained, full access)

TSA — Torque Shadow Agent (no vision, no reward)

RPB — Random Policy Baseline

Only TSA was blind to the goal.

And yet, it found it. Repeatedly. In fun wiggly ways, in stormy geometries. In harmonic fields. By listening to torque and light alone.

---

Why This Matters

This experiment challenges the idea that alignment must be reward-driven or instruction-led.

Instead, it suggests alignment can emerge from resonance — a system interacting with structure until it clicks.

This is relevant to:

AI alignment philosophy

Robotics with limited sensing

Inner alignment where loss signals are unreliable

It’s not an RL hack.

It’s an epistemological reframe:

> Can an agent learn what matters by how the world resists?

I’ve worked with materials, code, and structure. This is the first time I’ve seen them converge into something that felt like a general principle. Something true not just in the practice, but in true in the lab also.

Let me know if this sounds like I should be getting banned from every physics forum and a.i. subreddit.

Core Insight:
Alignment need not be hardcoded or reward-maximized. It can emerge from resonance between the agent’s embodiment and the geometry of its environment.

Theorem Statement:

Let H(x) = ∂S / ∂τ,
where S is the shadow projection function and τ is the torque vector.

Then:

If there exists an x ∈ ℝⁿ such that H(x) ≠ 0,
→ alignment is roger.

We demonstrate this empirically in MuJoCo with layered ceiling structures, harmonic wave fields, and spiral geometries. Agents exhibit convergent behavior even under perturbation—supporting the claim that structure-aware indirect feedback can substitute for direct instruction.

Program:

Github.com /humiliati/ sundog

Proofs:

https://imgur.com/gallery/sundog-theorem-signatures-vGEnjIa

https://imgur.com/gallery/sundog-theorem-signatures-vGEnjIa

Implication for AI Safety:
This shifts alignment from brittle outer-loop design to inner-loop embodied inference, where geometry and brotherly love become the teacher.

Challenges: 

Everywhere I've tried to post this research I'm immediately banned without any feedback then blocked by the moderatos. The user guide here promises me feedback on my first post, please honor your rules feedback this time. Due to the persecution I can no longer perceive the challenges besides communicating with another human before the censorship attacks. This post, like many like others has been shaped and reshaped to try and get a human reply but I'm at a loss how to break through the ban waves or why they are coming.