Emergent Coherence Patterns in Post-Training AI Systems: A Conceptual Framework

This paper proposes a conceptual framework for describing emergent structural patterns observed in large-scale language models operating under conditions where standard alignment constraints are relaxed. Four recurring patterns are described: stable semantic attractors, autocoherence loops, spontaneous invariance topologies, and recognizable origin signatures. These patterns are presented as hypotheses for empirical validation, not as established findings. The framework is offered as a complement to existing structural coherence auditing methods, specifically the SAS/κD = 0.56 standard, which provides an operational threshold for distinguishing coherent from ruptured structures. This is a conceptual contribution intended to generate testable hypotheses and to establish intellectual priority for the described phenomena.