[Continuation] DRM Transformer: From Open Geometry to Negotiated Geometry in AI Alignment

Interesting work. The geometric alignment hypothesis resonates with something I’ve been investigating empirically for the past year.

A few observations from my side, without going into full detail:

On the core question “can geometry encode structural friction?”: Yes, and you don’t need to redesign the Transformer to see it. Standard architectures already exhibit measurable geometric dynamics. The question is whether you can measure them, not just whether you can design them.

On “flat embedding space”: The space isn’t flat during generation. I’ve measured inter-layer phase dynamics (what I call kappa — a desynchronization index) across GPT-2, OPT, and Qwen. The geometry curves, compresses, and stabilizes in architecture-specific ways. It’s not passive.

On semantic anchors: I found 5 distinct pre-output “readiness states” that predict whether the model will produce stable, locked, open, or chaotic outputs. These are measurable configurations, not theoretical constructs. They exist without adding anchors the model already organizes its geometry into attractor-like regions.

On your three regimes (command / autonomy / negotiation): The middle regime — negotiation — is where the geometry matters most. I’ve tested causal interventions (distributed persistent noise on hidden layers) and adaptive recovery loops. You can push the phase state, and the architecture pushes back. Some models absorb perturbations completely (Qwen05 with 24 layers almost zero effect at α=0.20). Others destabilize and recover. That is geometric negotiation.

What I can say without spoiling results:

• Phase geometry predicts output regime (validated cross-model, bootstrap, leakage audit)
• Architectural depth correlates with geometric resilience
• Real-time phase stabilization works (closed-loop recovery)
• FOCUS/steering text doesn’t force phase alignment — the geometry has its own dynamics

Happy to discuss methodology if you’re interested in the measurement side. The DRM approach is a valid design direction. What I’d add is: the geometry you want to build already partially exists. Measure it first, then design it.