integrate McLeish et al. + Lys et al.: reposition related work, rung-2 Muon note

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

WORKSPACE_LOOPING.md

@@ -255,6 +255,16 @@ passing code, CE on code tokens, curriculum k=1 easy / 2 mixed / 4 hard; the
 
 ### Rung-2 design note: depth-graded band unfreezing
 
+**Optimizer note (from McLeish et al., arXiv:2511.07384):** use **Muon**, not
+AdamW, when unfreezing the band — they report AdamW loss-spikes to NaN when
+training through recurrence; adapter-only training didn't hit this, band-LoRA
+likely will. Also adopt their FLOPs convention for recurrent models
+(FLOPs = (6N₁+2N₂)D, N₁ = params with grads, N₂ = forward-only) in any
+compute-matched comparison. **Adaptive-α candidate (from Lys et al.,
+arXiv:2602.14759):** their training-free softmax auto-alignment interpolation
+is a cheap ablation against fixed α — directly relevant to the 12B α
+miscalibration.
+
 When the band itself is unfrozen (per-iteration LoRA), unfreeze
 **entrance-faded**: full trainability at L14 decaying to frozen by ~L22
 (e.g. LoRA α × max(0, 1−(ℓ−14)/8)). Rationale: the only novel inputs in the