AI & ML
No finite rulebook can certify every safe program: an impossibility theorem for gatekeeping AI agents
arXiv cs.CR0 views
arXiv:2611.13367v1 Announce Type: cross Abstract: An AI assistant writes small programs, and a gatekeeper must decide, from the code alone, whether each one may run. Suppose the rules are fixed: never perform a forbidden action, never let the output depend on a secret, always answer eventually. Can two rulebooks, one listing what is allowed and one listing what is not, both be complete, if every rule must be backed by evidence that a machine can check in finite time? We prove that they cannot. Checkable evidence can only establish what a finite record settles, and ``never'' and ``eventually'' are never settled by a finite record. For prohibitions the failure is one-sided: every violation can be caught by watching, but no safe program can be cleared for good. For confidentiality and responsiveness, neither side can be. We characterise exactly which rules about visible behaviour evidence can confirm, and we show that any sound rulebook of $n$ bits omits a compliant program of about $n$ bits. A time limit makes everything checkable, but only by running the program for essentially the whole limit, so a finite machine is not a feasible one. Finally, we show what a gatekeeper can still guarantee: not a verdict on every program, but a checkable list that contains everything it must allow and nothing it must refuse. Simulations on 2.2 million random programs and on self-referential adversaries show that the obstruction is adversarial rather than typical, and that the one error a longer test never removes is the liveness part of confidentiality.
Source:https://arxiv.org/abs/2611.13367PreviousVeritas: A Verifier Generation Harness for Agent TasksNextPulse or Provenance? What rPPG-Based Deepfake Detectors Learn
Related posts
Disentangling Forced and Internal Climate Variability in Single Realizations using Dynamic Mode Decomposition with Control
0arXiv stat.ML
Uniform-in-Time Weak Propagation-of-Chaos in Shallow Neural Networks
0arXiv stat.ML
Density-Ratio Losses for Post-Hoc Learning to Defer
0arXiv stat.ML
Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
0arXiv stat.ML
The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits
0arXiv stat.ML
SVP Is NP-Hard for Some Rank-2 Cyclotomic Modules
0arXiv cs.CR