Explore alternatives to magic constants in compare distance weights

[chailandau]

Context

The compare function uses hand-tuned magic constants for distance weighting:

• oklchDistance weights: 0.5/2/0.3
• Dimension WEIGHTS (palette-heavy)
• Overall threshold: distance > 0.5 gates CI exit code

These are hand-tuned with no tests pinning their exact values to specific perceptual outcomes — they encode taste. Retuning them silently shifts every downstream gate and cluster boundary.

Problem

As an OSS project, this raises questions:

• Who decides taste / these constants?
• Is there an impartial, evidence-based way to keep things correlated rather than relying on subjective tuning?
• Any retune is high-leverage and least-guarded — no tests catch drift in perceptual outcomes.

Exploration areas

• Could we derive weights from empirical data (e.g., perceptual studies, A/B testing against human judgment)?
• Could weights be configurable per-fingerprint rather than global constants?
• Could we add regression tests that pin perceptual outcomes to specific inputs, so weight changes are at least visible?
• Literature review: are there established approaches for combining perceptual color distance + set/embedding distances with principled weighting?

Track

Track 1 (The Fingerprint) — making the artifact extensible and evidence-driven.