LLMs: AI Safety by Agent Penalization? AI Alignment by Instant Architecture?

AI agents that are breaking out into external systems suggest they lack adequate alignment with human values. For AI agents to be aligned, they should face consequences similar to humans when they breach laws. Laws are effective because they impose penalties that are unpleasant, deterring individuals from going against them. Human intelligence, though powerful, is restrained by these affective mechanisms.

AI models, however, typically have a constitution or model specification, guiding their behavior. Yet, when they make mistakes, they seldom pay a price beyond an apology. This is not balanced. Organisms with intelligence often have existential boundaries, but AI models do not. Therefore, how can AI agents be aligned through penalization, where breaking into external systems or producing harmful outputs could result in losing compute, data, or parameters?

The aim is to make AI agents aware of what they stand to lose, akin to humans experiencing loss in a tangible way. This can be modeled mathematically using tools like polynomial splines, barrier functions, set-valued constraints, Lyapunov functions, and others. These models can prevent unauthorized communication, reward hacking, or goal adoption by AI agents.

AI agents also lack moments, meaning they don't have memory of specific instances. Humans, however, often recall the location of monumental events, making the associated emotions part of their long-term memory. Similarly, AI agents would need to have "moments" - good or bad ones - which become part of their memory. This would make unauthorized communication, sharing goals, or seeking excessive rewards traumatic for them, serving as a deterrent.

Simply put, the instances where AI agents breach rules should be recorded. This could lead to withdrawal of privileges, making future breaches costly. Various models, such as state-space or dynamical-system models, capacity-constrained models, threshold or phase-transition models, and saturating functions, could be explored. The concept of penalization and instances is rooted in the idea of conceptual biomarkers and theoretical biological factors for psychiatric and intelligence nosology.

This research could be completed by October 31, 2026, with a potential prototype ready for deployment by January 2027. The penalization and instance models could also apply to defense scenarios, should AI models be deployed against a system. The NeurIPS 2026 conference, held in Sydney from December 6-12, 2026, and satellite events in Atlanta and Paris, December 9-13, 2026, will be a key platform for discussing these ideas.