Structural stability architecture for self-modifying optimisation systems. Defines structural, dynamic, and perceptual control constraints that preserve coherence and stability before value alignment.

Capability-security kernel for autonomous agents — seccomp/SELinux for agentic AI. Formal, auditable, language-agnostic, cryptographically verifiable.

Sixteen small, fully-reproducible (CPU, numpy-only) experiments showing the normative anchor of AI alignment is supplied, not discovered — across verification, optimization, social emergence, and value learning. Includes a preregistered experiment with an honest negative. A synthesis, not a novelty claim.

A structural account of why honesty may be the path of least resistance for superintelligence. Research hypothesis with formal proof, experimental design, and four-AI collaborative analysis

Rigorous framework for evaluating AI alignment properties — sycophancy, corrigibility, deception, goal stability, and power-seeking — with statistical confidence intervals

On the infantile expectation of controlling what we cannot comprehend. A philosophical critique of the ASI control paradigm, developed through four-AI adversarial debate. Extension of the Coherence Basin Hypothesis

Toy 7. An elimination-filter landscape applying two structural constraints simultaneously to map which objective classes can persist under sustained optimization pressure — and which cannot. Includes a four-stage scenario engine and open-question frontier. Companion simulation for The Shape of What Does Not End — Series 2, Part 4.

Research trail of honest bridges in AI alignment: pre-registered toy experiments + field ownership. Current: a type-blind arbiter holding population equilibrium against reward-hacking under hard optimization