fix: correct CHROMATIC_TARGETS — canonical source + updated validation (92.3%)

README.md

@@ -38,15 +38,20 @@ The CDM is a companion to the base Refractor ONNX model (a multimodal fusion net
 
 ```
 Index  Color    CHROMATIC_TARGETS (temporal / spatial / ontological)
-  0    Red      Past-heavy / Thing-heavy / Known-heavy
-  1    Orange   Present-heavy / Thing-heavy / Known-heavy
-  2    Yellow   Present-heavy / Place-heavy / Known-heavy
-  3    Green    Present-heavy / Place-heavy / Known-heavy  <- same targets as Yellow
-  4    Blue     Future-heavy / Place-heavy / Forgotten-heavy
-  5    Indigo   Future-heavy / Future-heavy / Forgotten-heavy
-  6    Violet   Future-heavy / Future-heavy / Imagined-heavy
+  0    Red      Past / Thing / Known
+  1    Orange   Past / Thing / Imagined
+  2    Yellow   Future / Place / Imagined
+  3    Green    Future / Place / Forgotten
+  4    Blue     Present / Person / Forgotten
+  5    Indigo   Uniform / Uniform / Known+Forgotten [0.1, 0.4, 0.4]
+  6    Violet   Present / Person / Known
   7    White    Uniform across all axes
-  8    Black    Present-heavy / Thing-heavy / Imagined-heavy
+  8    Black    Uniform across all axes
+
+Targets are derived at runtime from `app/structures/concepts/chromatic_targets.py`,
+which reads directly from the canonical `the_rainbow_table_colors` Pydantic model.
+Previous versions had hand-rolled copies that diverged for 7 of 9 colours; this was
+corrected in April 2026 (fix-chromatic-targets-canonical-source).
 ```
 
 ## Validation Results
@@ -103,7 +108,7 @@ python training/validate_mix_scoring.py
 ## Limitations
 
 - CLAP embeddings have a maximum internal window of ~10s; chunked scoring is essential for full-length tracks
-- Green and White classification are unreliable (see validation results above)
+- Green classification is the weakest at 75% — two songs are near the Yellow/Violet boundary
 - Training data is drawn from a single artist's catalog — generalization to other music is untested
 - The concept embedding path requires a DeBERTa-v3-base inference pass (~600 MB model)