IndisputableMonolith.Measurement.RSNative.Alignment
This module defines an alignment protocol extending the core Protocol type with explicit invariants to ensure measurements remain comparable across different agents in the RS-native framework. Researchers formalizing multi-observer consistency or distributed recognition protocols would cite it when building on the Core scaffold. The module consists of type definitions for AlignmentProtocol, AlignmentMap, Alignment, and the apply function, constructed directly from the imported Core module with no proofs.
claimThe alignment protocol extends Protocol with invariants for cross-agent comparability, introducing types AlignmentProtocol, AlignmentMap, Alignment, and the operation apply.
background
The upstream RS-Native Measurement Framework (Core) supplies a Lean-first scaffold with ticks, voxels, coherence, action, and ledger observables at τ₀ = 1. Protocols carry explicit falsifiers so that measurement choices remain non-arbitrary. This module adds an alignment layer on top of those definitions to enforce comparability between agents.
proof idea
This is a definition module, no proofs.
why it matters in Recognition Science
The module supplies the alignment layer required by the broader RS-native measurement framework, feeding into higher-level recognition theorems that rely on cross-agent consistency. It directly implements the extension described in its doc-comment and supports the explicit-protocol design goal stated in the Core module.
scope and limits
- Does not define numerical calibration to SI or CODATA units.
- Does not prove invariance properties of the alignment maps.
- Does not specify concrete agent models or ledger implementations.
- Does not contain executable measurement procedures.