note: v6 dropped empirical heuristics

README.md

@@ -35,7 +35,7 @@ ASHQ1 is a post-training quantization method for GGUF models that uses an **imat
 | **ASHQ1** (v6) | Ornith-1.0-9B-MTP | 6012 MiB | **7.4697 ± 0.04862** | **−0.1551** |
 | Uniform Q6_K | Ornith-1.0-9B-MTP | 7198 MiB | 7.6248 ± 0.05039 | baseline |
 
-ASHQ1 beats uniform Q6_K by **0.155 PPL** while being **16.5% smaller** (−1186 MiB).
+ASHQ1 beats uniform Q6_K by **0.155 PPL** while being **16.5% smaller** (−1186 MiB). The current classifier (v6) dropped empirical depth-weighting heuristics — the theoretical priority queue now works even better.
 
 ASHQ1 is often on par with hand-tuned SHQ quants in quality, and sometimes surpasses them. At the same time, it saves significant time and effort — just set your target size and go.