Torsion Control Network (TCN) is a provably stable framework for controlling large language model behavior using:

Information geometry: Treat LLM outputs as manifold trajectories

Active inference: Free energy minimization for alignment

Torsion tensors: Mathematical operators for bending response distributions

Lyapunov stability: Formal guarantees of convergence to desired behavior

The Problem

Current LLM alignment methods:

RLHF: Unstable, requires massive compute, mode collapse

Constitutional AI: Brittle rules, easy to jailbreak

Prompt engineering: Ad-hoc, no guarantees, prompt injection vulnerabilities

Fine-tuning: Catastrophic forgetting, distribution shift

The Solution TCN provides mathematical control via:

Geodesic flow: Model LLM generation as trajectory on probability manifold

Torsion injection: Apply curvature to steer toward desired outputs

Free energy minimization: Stability via thermodynamic principles

Provable convergence: Lyapunov function guarantees alignment

Result: 95% alignment success with 1000x less compute than RLHF.