🧪 PatternSeek — Calibration + Bias

First open example of a federated calibration metric: CDI changes + cross-model delta + SRA bias gauge + provenance chain.

Federated calibration, made visible

This page is a live example of how PatternSeek treats language as a measurable control surface: naming variants are tested across multiple models, coherence shifts are quantified, and the full exchange is recorded as an auditable provenance chain (ChiR-IPP).

CDI: definitions & naming Cross-model delta SRA: reinforcement bias Provenance ledger

The key idea is simple: **LLMs can participate in peer-like review when given shared external controls**, generating an immutable, provenance-linked calibration ledger across federated models — without requiring centralized consensus.

What it detects

  • Semantic drag: when loaded terms produce inconsistent model behavior.
  • Tone-driven inflation: when positive framing yields “agreement” without evidence.
  • Stability under change: whether a renaming preserves the underlying math rigor.

Why it matters

  • Peer review readiness: reviewers can trace metric evolution and adoption status.
  • Cross-platform interoperability: calibrated definitions travel better between stacks.
  • Auditability: disputes become diffs + datasets + run IDs, not arguments.
View CDI page → Back to SOP →
Concept mock: Panels A–D (CDI ledger, cross-model delta, SRA gauge, ChiR-IPP provenance chain). Click to expand
Implementation note: this “ledger” can begin as Git + signed hashes + run manifests (JSON), then evolve into a dedicated store. The requirement is not the technology label — it’s the guarantee: traceable inputs → repeatable runs → verifiable outputs.