refact(comments): state the mechanism, not the incident

Every comment that invoked a 'law' (50 word-boundary sites across 28
tracked files) now states what is going on and why, in terms that
transfer to any project: gate twins drift until one side greenlights
what the other refuses; a tag re-points while a digest pins; a
restrictive engine behavior belongs to the runtime that imposed it and
must be re-tested, not inherited; a speculative head is tensors in the
file, not a flag; snapshot links dangle where the container resolves
them; exit 0 is a clean stop to systemd, so a self-exiting daemon on
on-failure goes dark reporting success. Event narratives and dates are
gone from these comments; the mechanics carry the argument.

Behavior-identical: bash -n, py_compile, yaml parse, full bats suite
408/408 green. One symbol rename rides along (stack script local
scale_law -> scale_contract, with its log text).

scripts/frank-archon-expert-activity.py

@@ -11,13 +11,13 @@ Tensor extents are DERIVED from consecutive GGUF header offsets — no quant
 type-size table (those are easy to get subtly wrong; a wrong per-element size
 silently under-covers each tensor and dumps tail samples into a junk bucket).
 
-Fleet law this instrument established (2026-09-26, 75 s window, 4 mixed
-streams, 2.7M expert-tensor samples): per-BLOCK read volume is FLAT
-(0.93x-1.08x of mean across all 40 CPU-resident blocks) because MoE top-k
-routing fires every block every token — there are no "most active layers" at
-`-ot` granularity. The real skew is per-EXPERT (top 10% of (block,expert)
-cells carry ~40% of samples), which tensor-level overrides cannot reach.
-Any 8 GPU-resident blocks are therefore equivalent; COUNT is the only lever.
+What this instrument settles: per-BLOCK expert read volume is FLAT across
+all CPU-resident blocks, because MoE top-k routing fires every block on
+every token — there are no "most active layers" at `-ot` granularity. The
+real skew is per-EXPERT: a small fraction of (block,expert) cells carries
+most of the samples, and tensor-level overrides cannot reach that level.
+Any fixed number of GPU-resident blocks is therefore equivalent; COUNT is
+the only lever.
 
 Usage (needs passwordless sudo for perf only; model files must be readable):
   fp-expert-activity.py <pid> [duration_s]        # dump + sample + analyze