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Engineering Bounded Halting and Observable Universality Tests

This paper reframes the halting problem as a bounded-halting decision with a step budget Taine, and shows that no deterministic checker can decide all instances within a fixed fraction of T below one. It also proposes adversarial tests for finitary universality that check resource growth and absence of fixed deliberation bounds, with implications for machine-learning systems.

AI Brief

Provides a concrete theoretical result on bounded halting and practical tests for universality claims in deployed systems.

arXiv:2611.06303v1 Announce Type: cross Abstract: The halting problem asks whether a program eventually halts, with unrestricted resources. Engineering systems instead certify bounded halting: given a program, an input, and a step budget T, decide whether the program halts within T steps. This paper studies two of its uses. First, it defines an exact finite-budget problem, placing the simulated program and the checker on the same unit-cost timescale, so the checker can be embedded in an adversarial program. A diagonal argument with explicit additive overhead shows that no fixed deterministic checker decides every instance within any fixed fraction of T below one, for large T. In the nondeterministic case, T bounds the branch depth allowed for a stable observable decision. Second, bounded-halting instances give adversarial tests for finitary universality, since a universal model must support arbitrary finite computations as T grows. The tests check resource growth with T, the absence of a fixed deliberation bound, and later access to intermediate data. They separate a fixed model with extendable external resources, a model family that grows with T, and a larger system built around a fixed component. For machine-learning systems, this identifies the resource growth a universality claim assumes and whether it fails for deployed models.
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