The headline f6_e2e_latency_bars only shows p90, hiding three regimes:
- mean: unified dominates (3.3s TTFT, 7.0s E2E vs sticky 5.6s / 12.1s)
- p50: sticky and unified are tied on first-turn TTFT (0.5s each) —
sticky's first turn of each session is free, after which queues
accumulate. Unified beats sticky everywhere else.
- p99: tail amplification reveals unified's biggest gap —
TTFT 42.3s vs sticky 74.1s; E2E 68.8s vs sticky 139.7s.
The 12-panel figure is the honest full picture; the 3-panel headline
stays for slide-friendly summary.
- analysis/characterization/window_1_results/raw_stats/{policy}.json:
cached ttft/tpot/e2e {mean,p50,p90,p99} pulled from dash0
/home/admin/cpfs/wjh/agentic-kv/outputs/b3_sweep_20260525_095043/
(b3_policy_comparison.json doesn't record mean, only percentiles).
- analysis/characterization/render_window1_figures.py:
new fig_b3_latency_full_grid renders the 4×3 grid from the cache.
- figs/f6_e2e_latency_full_grid.png: 12-panel companion.
- PAPER_OUTLINE.md §5.2: both figures embedded; main table column
renamed from "Hotspot idx" to "Worker p90 (median / max)" to match
the new metric convention.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
After the B3 audit bug fixes (joined_analysis hotspot median +
b3_analyze percentile interp), regenerate b3_policy_comparison.json
and the per-policy hotspot_index.json from the same raw run on
dash0 and re-render the three affected figures (apc-vs-hotspot,
latency-bars, per-worker TTFT).
Key number changes in window_1_results.md:
- hotspot_index magnitudes corrected (all five policies; lmetric
smallest delta at +0.7%, sticky largest at +16.1%)
- "capped reduces hotspot 13%" -> "~10% (2.253 -> 2.020)"
- TTFT/E2E/TPOT percentiles shift by <1% from floor->interp
(unified TTFT p90 7.24 -> 7.35 s)
Restructured "Caveats" into "Limitations (read this before quoting
B3 numbers)":
1. Agentic dispatch coupling is by design — promoted from caveat
to top-level methodology framing, tied to
agentic_dispatch_coupling.md
2. B3 interference_index is binary (not size-graded) — added
3. Hot-sweep cache contamination (<1%) — kept
4. Unified interference unrecoverable — kept with explicit warning
not to read unified's failure attribution as causal
5. w600 is a sample, not full trace — kept
6. Reuse decomposition is per-token in expectation — added
current_results/characterization_claim_matrix.md updates:
- The "heavy-tail not sole cause" claim now cites the corrected
~10% drop with the median bug noted
- New supported claim: "B3 saturated-replay latency gaps include an
agentic dispatch-coupling feedback term, which is intentional and
matches production"; cited against agentic_dispatch_coupling.md.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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>
