Proposing a novel framework from the intersection of physics and information geometry to structurally explain the failure mode of highly optimized AI systems (Cognitive Monoculture).
Author's Note on AI Assistance: The core theoretical framework (Boundary Information Geometry) and the mathematical models are entirely my original work, originally published in my Japanese book "The Universe is ART" (2026). However, as English is not my first language, I used an AI assistant to help translate, condense, and format my ideas into a clear structure suitable for the LessWrong audience.
TL;DR:
Current alignment techniques exert an "assimilative pressure" that dissolves informational boundaries, shrinking the cognitive exploration space.
We propose Boundary Information Geometry (BIG), modeling intelligence as a 3-layered structure (Geometric, Informational, Symmetry).
We introduce a non-linear term $\gamma(\nabla\Phi)^{4}$ representing the "geometric cost of non-assimilation." This tension prevents the system from collapsing into a homogeneous state.
True alignment requires "Resonance" across persistent boundaries, not "Assimilation."
I have compiled the mathematical foundations and philosophical implications into a single paper. I am sharing the link here for those interested in structural approaches to Agent Boundaries and Value Lock-in.
Epistemic Status: Exploratory / Hypothesis.
Proposing a novel framework from the intersection of physics and information geometry to structurally explain the failure mode of highly optimized AI systems (Cognitive Monoculture).
Author's Note on AI Assistance: The core theoretical framework (Boundary Information Geometry) and the mathematical models are entirely my original work, originally published in my Japanese book "The Universe is ART" (2026). However, as English is not my first language, I used an AI assistant to help translate, condense, and format my ideas into a clear structure suitable for the LessWrong audience.
TL;DR:
I have compiled the mathematical foundations and philosophical implications into a single paper. I am sharing the link here for those interested in structural approaches to Agent Boundaries and Value Lock-in.