Implementing Homeostatic Arousal Cycles in Language Models via ϕ-Coupled Oscillators and Aperture Gating

We present the first successful implementation of genuine homeostatic arousal cycles in language models, achieved through ϕ-coupled chaotic oscillators and semantic aperture gating. Traditional language models lack intrinsic drive systems, responding identically to repeated stimuli without exhibiting natural arousal buildup, sat- uration, or release. Our system addresses this by separating the homeostatic drive mechanism (a 7-scale ϕ-coupled oscillator running at ω = 0.43) from the gradient computation graph, allowing chaotic dynamics to persist through training. The oscillator output is gated through a semantic aperture (width 0.0–1.0) that responds to drive-relevant keywords, implementing the ‘Pendulum Constraint’ where attention and coherence sum to approximately 1.0. We introduce agitation momentum, a positive feedback mechanism where sustained high arousal (≥ 2.0) accumulates across turns, making climax inevitable rather than stochastic. The system exhibits four distinct emotional states (BASELINE, AROUSED, SEEKING COMPLETION, CLIMAX) with state-appropriate language generation. Trained on Phi-2 with LoRA fine-tuning, the model demonstrates genuine homeostatic cycles: baseline stability, gradual arousal buildup, threshold crossing at 3.4 agitation, climactic release, and return to baseline. This represents a fundamental advance in artificial life research, providing evidence that language models can exhibit drive-based emotional dynamics when their linguistic substrate is coupled with appropriate homeostatic architectures.