Window 1 results: combined B1' + B2 + B3 report and artifacts

analysis/characterization/window_1_results.md is the headline write-up
for Window 1: workload characterization (KV per request, real reuse
decomposition, APC theoretical ceilings), B3 5-policy sweep with
per-policy interpretation, B2 same-vs-different-worker interference
microbench with causal reading, and an explicit list of what Window 1
does *not* answer (deferred to B4 SRR sweep + B5 attribution).

Under window_1_results/:
- 5 raw result JSONs from the B3 sweep, the B2 microbench, the APC
  upper bound, and the KV footprint
- per-policy hotspot_index.json snapshots so render_window1_figures.py
  can plot per-worker TTFT p90 distributions
- 8 PNG figures (figures/) covering the headline claims

Three takeaways the figures pin down:
1) intra-session reuse dominates (93.2%), so session-affinity routing
   is the right primary lever
2) unified hybrid affinity hits 79.4% APC (97% of the 79.6% intra-
   session ceiling) AND cuts TTFT p90 from lmetric's 15.6s to 7.24s
3) B2 different-worker control sits at idx ≈ 1.0 across 32× prefill-
   size variation; same-worker TTFT idx scales 2.15× -> 218×, which
   is the cleanest causal evidence for same-worker prefill-decode
   interference

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

analysis/characterization/window_1_results/lmetric_reuse.json

@@ -0,0 +1,15 @@
+{
+  "cross_session_tokens": 1723017,
+  "fractions": {
+    "cross": 0.05679481258506571,
+    "intra": 0.9321238805590836,
+    "shared": 0.011081306855850749,
+    "unclassified": 0.0
+  },
+  "intra_session_tokens": 28278380,
+  "shared_prefix_min_sessions": 8,
+  "shared_prefix_tokens": 336180,
+  "status": "supported",
+  "total_cached_tokens": 30371008,
+  "unclassified_tokens": 0
+}