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This research establishes fundamental mathematical limits on verifying the safety of recursively self-improving AI systems, proving that no total algorithm can simultaneously be sound, complete, and tractable for Turing-complete code-modifying agents. The study demonstrates that dynamic safety certification faces undecidable barriers similar to Rice's theorem, implying that robust oversight requires restricting system expressivity rather than relying on hierarchical supervision towers. These findings are critical for understanding the theoretical boundaries of autonomous AI evolution and the feasibility of formal safety guarantees in advanced models.
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Algorithmic Unverifiability of Safety for Fixed and Recursively Self-Improving Systems

arXiv3 min read

arXiv:2606.28639v3 Announce Type: replace-cross Abstract: We establish mathematical limits of algorithmic safety verification for Turing-complete self-modifying systems, the class in which recursive self-improvement takes place, both for a fixed system and across its own modification. Statically, no verifier is sound, complete and tractable: over unbounded domains by Rice's and G\"odel's theorems, over all finite configurations by Trakhtenbrot's theorem, and over succinctly described finite environments because verifying a policy against an adversary is coNP-complete and synthesising one is PSPACE-complete. Dynamically, we model one step of self-modification as a computable transformation of code and ask whether a safety property survives it. If the transformation depends only on behaviour, this is Rice's theorem one level up; if it reads the code, as self-modification does, the question is no longer semantic, yet the same s-m-n reduction works inside a class of behaviourally identical programs and inherits the halting degree. One step is never harder than the property; persistence along the whole trajectory can be $\Pi^0_2$-complete. Certification by a total algorithm is possible only for transformations of restricted expressivity, not merely for systems that stop changing. No tower of supervisors helps, and every total supervisor errs on an undecidable set of systems. For effectively pointwise properties, every faithful bounded scheme that certifies on finite behavioural evidence admits evolution traces certified at every stage while the property is violated. What survives is exact: a monitor that raises an alarm on violation semidecides it, and comparison against a frozen reference keeps the full theory.

Abstract:We establish mathematical limits of algorithmic safety verification for Turing-complete self-modifying systems, the class in which recursive self-improvement takes place, both for a fixed system and across its own modification. Statically, no verifier is sound, complete and tractable: over unbounded domains by Rice's and Gödel's theorems, over all finite configurations by Trakhtenbrot's theorem, and over succinctly described finite environments because verifying a policy against an adversary is coNP-complete and synthesising one is PSPACE-complete. Dynamically, we model one step of self-modification as a computable transformation of code and ask whether a safety property survives it. If the transformation depends only on behaviour, this is Rice's theorem one level up; if it reads the code, as self-modification does, the question is no longer semantic, yet the same s-m-n reduction works inside a class of behaviourally identical programs and inherits the halting degree. One step is never harder than the property; persistence along the whole trajectory can be $\Pi^0_2$-complete. Certification by a total algorithm is possible only for transformations of restricted expressivity, not merely for systems that stop changing. No tower of supervisors helps, and every total supervisor errs on an undecidable set of systems. For effectively pointwise properties, every faithful bounded scheme that certifies on finite behavioural evidence admits evolution traces certified at every stage while the property is violated. What survives is exact: a monitor that raises an alarm on violation semidecides it, and comparison against a frozen reference keeps the full theory.

v3: revised & retitled. Part II covers behavioral/code-reading self-modification, framing safety-generality via restricted expressivity (not stasis); supervisory regress drops Turing-complete assumption. Resource face: coNP-complete verification, PSPACE-complete synthesis over succinct arenas. Part III assumes effectively pointwise properties. 30 pp. Companion: arXiv:2609.11326

Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Computation and Language (cs.CL)

I.2.0; F.1.1; F.1.3; F.4.1; D.2.4

arXiv:2606.28639 [cs.LO]

(or arXiv:2606.28639v3 [cs.LO] for this version)

Full text under the publisher's open licence · 310 words

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