Allostatic Control Systems: Goal Governance in Changing Environments

What the math gives to ML

The paper proposes an allostatic controller with an explicit, slowly governed reference rather than a permanently fixed goal: a fast policy acts under the current reference while a slow policy updates it from delayed outcome evidence. Its transferable mechanism is the separation between service regulation and goal governance, together with the warning that evidence latency can make correction arrive after the environment has changed again. In neural networks, this can become a meta-controller that updates task weights, target statistics, prompts, or auxiliary objectives only after collecting eligible outcome evidence, while the base model continues fast optimization. The key falsifiable prediction is a latency-versus-environment-drift boundary: adaptive goal governance helps only when the reference-correction delay is shorter than the time over which the current objective becomes harmful.