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>
The B2 same-worker TPOT p90 idx is non-monotone: 7.89x at 32k drops
to 2.26x at 65k. The naive reading is "interference gets weaker for
huge prefills"; the actual mechanism is a regime shift, and reading
TPOT p90 alone is misleading.
Three superimposed effects:
1. Cost migration TPOT -> TTFT. A 32k prefill is short enough that
chunked-prefill keeps interleaving decode steps, so overlapping
decodes trickle tokens out at painful per-token rates. A 65k
prefill is long enough that overlapping decodes are *fully*
blocked for ~10s; once they break through, the injection is
winding down and subsequent iterations run unobstructed. The
cost lands on the TTFT clock (14s) instead of inflating TPOT.
2. Bimodal TPOT distribution. At 65k overlap, decodes split into
"blocked entire prefill then normal rate" and "trickled slowly
through prefill chunks". p99 sits on the second population and
grows 59 -> 169.5 ms; p90 sits on the first and shrinks.
3. "Clean" stops being clean. With 4x ~10s injections in 60s, the
110 "clean" decodes at 65k are squeezed into 2-3s recovery
pockets. TPOT p90 clean rises 6.9 -> 9.6 ms (40%), shrinking
the denominator of the ratio.
window_1_results.md adds a new B2 subsection laying out the
mechanism with the per-cell data table and the explicit reading
rule: headline interference metric is TTFT idx (monotone); TPOT
p99 is the right tail indicator; TPOT p90 alone is unsafe across
regime shifts. Direct implication: TTFT and TPOT need separate
SLO thresholds under PD-colo, because they measure costs from
different points in the request lifecycle and the cost migration
between them is workload-dependent.
current_results/characterization_claim_matrix.md adds a new
supported claim for the cost migration, listed against the existing
B2 evidence. current_results/reviewer_risk_register.md adds a
low-severity entry warning future readers off TPOT p90 alone.
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>
