Maruf Sharker Munna

Generative models retrained on their own output narrow their distribution and lose the tails first, and training pipelines have lacked a way to correct this while they run. HOMEOSTASIS fuses signed, hash-chained provenance records with three model vital signs, predictive entropy, calibration error and representational drift, into a Lyapunov-style collapse-risk score, and a PID controller adjusts the human-data mixing ratio and quarantine decisions. On a five-dimensional Gaussian recursion over 30 seeds and 20 generations, closed-loop control lowered final collapse risk from 2.67 to 0.48 and held tail coverage at 97.4% against 66.2% without control, while provenance-gated quarantine showed no detectable benefit beyond control alone.

Generative models retrained on their own output narrow their distribution and lose the tails first, and training pipelines have lacked a way to correct this while they run. HOMEOSTASIS fuses signed, hash-chained provenance records with three model vital signs, predictive entropy, calibration error and representational drift, into a Lyapunov-style collapse-risk score, and a PID controller adjusts the human-data mixing ratio and quarantine decisions. On a five-dimensional Gaussian recursion over 30 seeds and 20 generations, closed-loop control lowered final collapse risk from 2.67 to 0.48 and held tail coverage at 97.4% against 66.2% without control, while provenance-gated quarantine showed no detectable benefit beyond control alone.