arXiv:2606.30975 simulations document hysteresis loops in adaptive gain for artificial agents under reversed uncertainty targets

The model drives an agent through continuous uncertainty target shifts then reverses direction without state reset. Forward and return paths produce distinct control demands despite convergence to the same terminal state. Gain required during return from high-demand regimes exceeds the gain observed on the initial ascent, confirming path dependence rather than memoryless response to instantaneous targets.

State coherence remains comparable across directions, isolating the effect to control burden. Anticipatory stabilization before disturbance exposure further reduces peak gain relative to post-disturbance recovery. These patterns align with documented control theory results on systems with delayed feedback and internal state accumulation, extending them to autonomous agent governance.

Current AI policy frameworks evaluate output stability without auditing cumulative regulatory load. Hysteresis implies that certification based solely on endpoint behavior will underestimate oversight costs for agents whose histories include high-uncertainty excursions. Operational monitoring must therefore track control effort trajectories, not merely final states, to avoid under-provisioning regulatory capacity.

Deployment records from multi-agent platforms already show elevated correction frequency following regime shifts; extending those logs to include gain histories would allow direct measurement of the predicted loops.