mlsanityData to 7 Oct 2026

Algorithmic Unverifiability of Safety for Fixed and Recursively Self-Improving Systems

arXiv 2606.28639 ↗First posted 26 Jun 2026cs.LO cs.AI cs.CCcited by 1

Not on the map. Why?

Abstract (arXiv)

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 Pi02-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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 40 references that are in the map. First posted on arXiv 26 Jun 2026.

Builds on

9 of 40 references.

References · 9

  1. 2016Concrete Problems in AI Safetycited by 1,582
  2. 2018Scalable agent alignment via reward modeling: a research directioncited by 438
  3. 2017Safe Reinforcement Learning via Shieldingcited by 427
  4. 2018AI safety via debatecited by 384
  5. 2025Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agentscited by 294
  6. 2018Supervising strong learners by amplifying weak expertscited by 139
  7. 2016Towards Verified Artificial Intelligencecited by 36
  8. 2021Impossibility Results in AI: A Surveycited by 13
  9. 2026No Certificate for Alignment: Two Independent Impossibilities and the Pareto Frontier of Achievable Safety Guaranteescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.