Yuri Downing · A portfolio of questions, proposals, and critical inquiry developed with AI
Working draft · September 24, 2026
I am interested in how increasingly independent AI systems can learn, cooperate, and remain accountable as they change. My contribution begins with questions: what future are we creating, what assumptions are we carrying into it, and what would make a different approach worth trying?
I am not a software engineer. My coding work has been produced with AI assistance. I want to contribute through conceptual development, scenario analysis, and the judgment required to turn an intuition into a proposal that others can examine and test.
This portfolio begins with one case study. Its purpose is to make my thinking available for scrutiny, including where it remains incomplete.
The name comes from conversations that produced the Recursive Path writings. In my account of that collaboration, the assistant generated much of the original language and many of the ideas; I questioned, redirected, and helped develop them. I identify periodic return and exchange between diverging branches as a contribution I helped shape.
The surviving Scribe’s Foreword attributes the text to a language model responding to my questions. The original chats have not yet been recovered in this review, so exact attribution cannot be reconstructed turn by turn.
The earlier work used theological and poetic language to explore shared commitments. Here, those metaphors become questions about design and governance. They are not evidence that an AI system has subjective experience.
How can independently operating AI agents share useful learning without spreading the same mistakes or erasing adaptations that matter locally?
An agent begins from a shared, versioned foundation and operates in a particular environment. It accumulates experience and may develop different memories, methods, or proposed instructions. At intervals suited to its task, it returns a record of what changed, why it changed, what happened, and what remains uncertain.
A review process considers those contributions. Accepted improvements become a new shared version; locally useful adaptations can remain local. The returning agent receives a compatible reviewed update while preserving the provenance of its own experience.
The ambition is continuity through exchange: differences remain legible, and cooperation remains possible.
The first experiment would exchange task memories and proposed operating instructions. Source-code changes and changes to model weights require different review and deployment mechanisms and are outside this initial proposal.
A shared version would distinguish required operating boundaries from optional practices. The review process would record who accepted a change, the evidence used, and how that decision could be challenged.