Emergenz in LLM-Verläufen als operatorgebundene Makroform

This theoretical working paper develops a model of emergence in long-form interactions with large language models (LLMs). Rather than treating emergence as an isolated output effect, a sudden capability jump, or an internal property of the model alone, the paper describes emergence as possible macrodynamics of a coupled human-model system.

The paper is positioned at the intersection of LLM emergence research, Human-AI Interaction, context engineering, and dynamical systems perspectives on long-form interaction.

The central assumption is that there is no direct channel of influence between operator and model beyond context. The model processes context; the operator shapes context. Through repeated feedback, correction, reopening, condensation, transition control, and reconstruction, a coupling regime may emerge that cannot be attributed solely to the model or solely to the human operator.

The paper introduces five conceptual criteria for emergent macroforms in LLM trajectories: form persistence, corridor organization / trajectory binding, irreducibility, reconstruction, and phase-like activity structure. These criteria are used to distinguish emergent process orders from local coherence, prompt effects, simple repetition, or mere context accumulation.

The present version is based on the author’s own observation practice with longer LLM interactions and develops a theoretical model for analyzing stable LLM trajectory dynamics. It does not disclose technical measurement architecture, internal evaluation procedures, thresholds, or protected operationalizations. A separate empirical validation of the model is reserved for future work.

Language of full text: German.