Paper: An Enclosed Mode Is a Gauge Choice: Topology Relative to Reach in Cert... Field: Machine Learning Author: Javier Aguilar Martín Published: 2026-08-28 arXiv: 2608.28541
Abstract
A code world model accepted by a sampling gate can be exactly right on everything the gate can see and arbitrarily wrong beyond it. This paper characterizes what a certified model can know, and what its errors can cost, when the omission is an annular freeze mode enclosing an unreachable interior.
The gate quotient makes the question precise: acceptance-with-certainty determines the model exactly on the reachable query set; beyond reach is gauge. On a minimal ring instrument, the author proves the extreme case — a wrong-topology filled-disc artifact unfalsifiable by any sampling gate and bitwise harmless at play — and measures, with LLM synthesis across three model families, how one knob (a channel of width gamma) walks the same artifact through three regimes: unfalsifiable-and-harmless, falsifiable-and-costly, and immediately falsified.
Key Findings
Three principles organize the empirical results:
1. Danger is topological relative to reach. A channel that the planner can use collapses the blind model's exploitation (runtime cost drops from 1.09 to ~0, with a knee at gamma ~ 0.1), while a hidden channel with the same first Betti number retains its full strength (1.12).
2. Repairs are parameter-limited and sensor-limited. No model family can recover the region from external evidence; from the interior, models propose the correct topology but cannot fix its parameters, and the proposed topology tracks an erroneous beta_1 of the guided persistent-homology summary (a sensor with geometric resolution limits) rather than the truth.
3. Mitigation must match the dimension and direction of the error. Point fences fail against one-dimensional boundaries; dimension-matched persistent fences collapse exploitation into a two-lesson transient (0.999 to 0.058), and dual-free certificates symmetrically collapse invented-mode failures (1.769 to 0.029). In n dimensions, shells make misidentification nearly certain while the danger remains fully exploitable: the two axes are independent.
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