Governed Abundance in AI-Assisted Research: Instruction Stabilization, Transcript Memory, and the Control of Human-AI Togetherness

This paper examines a central problem in AI-assisted research: large language models can generate abundant, locally plausible ideas, drafts, continuations, and candidate research objects faster than ordinary human instruction and review can reliably govern them. The paper argues that the resulting challenge is not primarily one of generation but of realization. Drawing on the Reflexive Laboratory research program, it introduces governed abundance as a framework for understanding how human intent, instruction stabilization, transcript memory, review capacity, and quality-control mechanisms convert AI-generated potential into realized research value. The paper develops a distinction between current-object control and future-option control, proposes pruning, grafting, and re-entry as mechanisms for preserving future research value, and situates these ideas within broader discussions of AI-assisted knowledge production, scientific workflow design, and research governance. The central claim is that control is not opposed to abundance; it is the condition that makes abundance usable.