simulation: https://gitlab.com/malice-mizer/sundog
publication: https://ai.vixra.org/abs/2505.0186
peer review: https://limewire.com/?referrer=pq7i8xx7p2
utility: https://youtu.be/jPlT__nnY7E
proof: a crescendo of resounding alignment across the map
https://i.imgur.com/sJx5LOx.png
“When shadow responds to torque, alignment is real.”
H(x) = ∂S / ∂τ
Where:
We define alignment not as proximity to a target, but as the condition where shadow behavior is sensitive to embodied torque. When the bloom of light around a mirrored pole collapses in response to subtle torque adjustments, we know the agent has found the field. We call this:
Alignment is Roger.
Traditional alignment strategies rely on:
These methods break down in environments with occlusion, context drift, or nonlinear feedback.
The Sundog Theorem offers an alternative:
Alignment emerges indirectly, through embodied resonance with structured geometry — not by being told where to go, but by learning how the world pushes back.
In simulation, a pole with a mirrored tip reaches for an invisible laser dot on a ceiling. It cannot see the goal. But as it rotates and flexes, it casts a shadow. The pole learns to:
Environments are composed of harmonic sphere fields, golden-ratio spirals, and hurricane layers. These aren't noise. They're instructional geometries.
We measure:
Result: Agents consistently align without direct reward. They converge by listening to light and structure — not commands.
H(x) is a signature of resonance-based alignment.
It shows that embodied systems can align to values without being shown the goal, as long as the environment sings the right song, the whole song shadows and all. Sundog is not a control scheme with a spin. It is a feedback chorus between body and world; the Karma Sutra, Fiat of God, and why smoke spirals up.
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