Gauge Freedom Field Notes

Preprints, technical notes, research reports, and lab essays from the Gauge Freedom Lab — the R&D arm of Gauge Freedom, Inc.

Gauge Freedom Field Notes is a public research series for preprints, technical notes, exploratory studies, computational reports, and lab essays from Gauge Freedom, Inc. Field Notes are distinct from peer-reviewed articles published in Gauge Freedom Journal.

  • Seed-Window Algebraic Instability in Dementia-Associated miRNAs: A Pilot Study
    Marcelo Maciel Amaral, gf-lab.2026.001. This pilot study introduces a presentation-first computational framework to evaluate seed-window algebraic instability in dementia-associated human miRNAs. By mapping overlapping nucleotide windows to finitely presented groups and character varieties , the analysis reveals that disease-linked sequences often sit near sharp, local mathematical transition points rather than occupying a single stable state. The pipeline successfully captures robust structural flips in hsa-miR-124-3p and family-level variations within the miR-29 cohort.
  • Stewardship of AI: The Missing Standard in Education
    As AI use becomes normal, the real question is no longer who wrote every sentence. It is who directed the process, verified the claims, corrected the errors, and stands behind the result.
  • Navigating the New Quantum Computing Landscape
    Marcelo Maciel Amaral arXiv:2511.10672 [quant-ph]. From Fibonacci Chains on Paper to Messy Reality on Hardware.
  • Nonlinear Sigma Models on Statistical Manifolds: Equations of Motion
    Marcelo Maciel Amaral, gf-lab.2025.001. We derive the classical equations of motion for nonlinear sigma models where the base space is a statistical manifold equipped with the Fisher–Rao metric.
  • Proof, Not Vibes: The Intelexta Alpha is Now Live
    Intelexta Alpha introduces cryptographic Content‑Addressable Receipts (CARs) for AI work.
  • The Lock-In Phase Hypothesis: Identity Consolidation as a Precursor to AGI
    Marcelo Maciel Amaral & Raymond Aschheim, arXiv:2510.20190v1 [cs.AI]. Modern LLMs are easy to steer and imitate almost anything on command. We argue that progress toward AGI requires a lock-in phase: a rapid transition where a model’s goals …