🤖 AI Summary
This study addresses the lack of binding between intervention directions and named semantics in latent spaces, as well as the problem of coordinate ambiguity. To this end, it proposes an anchor observer framework grounded in known perturbation signatures. Methodologically, by integrating capacity-matched least-squares prediction, a coordinate transport algorithm, a rank-aware abstention strategy, and fault injection testing, the approach effectively decouples prediction, semantic support, and scientific validation. Synthetic vascular auditing experiments demonstrate that this mechanism achieves predictive equivalence at 1e-15 precision, successfully identifies all injected errors, and rejects unsupported interpretations. Consequently, this work establishes a rigorous, executable separation paradigm for interpretable editing.
📝 Abstract
Complete orthogonal predictive coordinates do not by themselves bind a latent direction to a named intervention. We present a mathematical and synthetic audit motivated by vascular device-vessel suitcordance. Capacity-matched least-squares predictors were exactly equivalent under complete fixed output transforms, whereas an anchor-only observer recovered interpretations only within the span of known perturbation signatures. Six three-dimensional configurations across 64 seeds gave a maximum paired prediction discrepancy of 6.7e-15 but a median untransported edit error of 1.513. Coordinate transport removed that error. Noisy and weak anchors constrained calibration stability, and changing the representation basis required recalibration or verified transport. Across 256 additional fits in dimensions 3-24, prediction equivalence persisted within 4.0e-15. We then evaluated nine deliberate runnable fault classes across 64 seeds. All 576 faulty executions completed, but each violated at least one reconstruction, prediction, delivered-edit or scope contract; all 320 valid control records passed. Repeating a faulty implementation gave exact self-agreement despite error against the separately computed simulator expectation. For one omitted-direction defect, probe coverage followed its analytic law, and rank-aware abstention protected unsupported interpretations. Scalar-noise experiments exposed both missed weak faults and excessive rejection under narrow relative tolerances. These controls provide an executable separation of prediction, semantic support and scientific acceptance. They are synthetic numerical audits, not clinical validation, neural JEPA-Anything replication, agent learning or patient treatment-effect estimation.