Fiber Fingerprints of Hidden Learning-State Dynamics
A learning system can occupy execution states that are indistinguishable under every declared present-behavior readout yet respond differently to future training. We formalize this through fiber fingerprints: controlled future-learning response laws restricted to present-behavior equivalence classes. Prefix-compatible finite probes induce a predictive quotient functor, a Nerode-type minimal recursively sufficient representation, and a canonical set-level predictive fiber without assuming smoothness, reversibility, finite rank, or a manifold. Under an explicit finite-dimensional Hilbert realization, response decomposes into visible, visible-mode-reuse, and irreducible-new sectors; a histor...
| Authors | Qinyou Wang |
|---|---|
| Primary category | cs.LG |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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