Full corpus (paper + OTTC spec + ChaOS demo + supporting docs) is here: https://t.co/MAwLYxG4Lz
Looking for roles, collaborations, or conversations where this kind of systems thinking is valued.
Happy to discuss qualification assays, migration paths, or how the rectangle invariants scale.
#AIGovernance #LLMAgents #LongHorizonAI #AIAlignment #AISafety #AgenticAI #MutationBoundaryProblem #OTTC
I’ve been working on something I believe is under-specified in long-horizon AI systems.
Not just “how do we make better outputs” or “how do we add memory/tools,” but the deeper question of how stochastic, opaque generation is allowed to become durable change in a shared world across time.
I call it the Mutation Boundary Problem.
This is framed as an open research invitation. I’m not credentialed in the traditional sense — I’m a 40-year tabletop GM who iterated this with LLMs as thinking partners while working full-time.
If the Mutation Boundary Problem and OTTC-shaped governance resonate with what you’re building (long-horizon agents, narrative systems, enterprise governance, safety), I’d love to hear from you.
Complete observability may be impossible, and in any case is not the same thing as lawful control. A governable system need not be perfectly transparent; it must instead be constrained at the point where interior generation becomes socially durable action.
When semantic generation is constrained by lawful transitions, bounded authority, and auditable commit, continuity emerges without requiring full observability of the generator’s internal manifold.
I don't have a solution, I do have a research program idea I'd like you to consider that places the problem at the mutation boundary between stochastic cognition and durable state.