IndisputableMonolith.Measurement.RSNative.Alignment
The Alignment module defines protocols extending the base Protocol type with invariants that enforce cross-agent comparability of RS-native measurements. Researchers constructing multi-agent or distributed recognition models would cite these definitions when requiring consistent observables across observers. The module is purely definitional, introducing AlignmentProtocol, AlignmentMap, Alignment, and an apply function on top of the imported Core framework.
claimAn alignment protocol extends a Protocol with explicit invariants ensuring that for any agents $A$ and $B$ the measurement maps satisfy cross-agent comparability conditions.
background
This module sits inside the RS-Native Measurement Framework introduced by the Core module. That framework supplies a Lean-first scaffold built from ticks, voxels, coherence, and action observables with fundamental time unit $τ₀ = 1$; SI/CODATA values remain optional external calibration. Protocols are required to carry explicit falsifiers so that measurement choices stay non-arbitrary.
proof idea
This is a definition module, no proofs.
why it matters in Recognition Science
The module supplies the alignment layer required by the Measurement domain of Recognition Science, directly supporting the Core module's design goal that every measurement carries a protocol plus falsifiers. It prepares the ground for downstream constructions that need consistent observables across multiple agents.
scope and limits
- Does not introduce any theorems or proofs.
- Does not define numerical calibration to SI units.
- Does not address single-agent measurement without cross-comparison.