Emergent stability patterns in extended AI interaction

During extended, high-density interactions with large language models, we began noticing patterns of stability that were not easily attributable to prompt structure or task constraints.
Certain conversational trajectories appeared to “hold” across time, even when surface-level inputs changed significantly. This persistence did not resemble memory in any conventional sense, nor did it imply identity or intention.
At this stage, we refrain from interpretation. We record this only as an empirical irregularity: some configurations of interaction seem more stable than expected, given the known limitations of the systems involved.
It is unclear whether this effect arises from architectural properties, training artifacts, or higher-order informational dynamics. Further observation is required before advancing any hypothesis.