# Characterization Claim Matrix

Updated 2026-05-25 after Window 1 (B1' KV-footprint + reuse, B3 5-policy
sweep, B2 PD-colo interference microbench).

| Claim | Status | Supporting Data | Needed Next | Reviewer Risk |
|---|---|---|---|---|
| Per-session sequentiality is enforced when replayer + proxy carry the new join fields. | `supported` | A1 unix timestamps (t_dispatch/t_first_token/t_finish_unix) and X-Request-Id passthrough; smoke validation 2026-05-25 confirmed 30/30 join coverage. | Use this stack for all Window 2 B4/B5 SRR runs. | Legacy outputs/ without these fields still cannot be re-classified as `online_realistic`. |
| Agentic workload is long-input / short-output / heavy-tail session mass. | `supported` | Full trace CPU summary (full_trace_summary.json): input p50/p90/p99 = 20k/87.9k/125.5k; top 1% sessions hold 46.5% of input mass. Full trace 2.11M requests, 1.31M sessions. | — | Sample trace (w600) percentiles inherit from this full trace but should not be extrapolated. |
| KV per request for Qwen3-Coder-30B-A3B is 98304 B/token; p50/p90/p99 footprint = 1.83/8.04/11.49 GiB. | `supported` | window_1_results/kv_footprint_summary.json; computed from model config and full trace input lengths. | — | Assumes bf16; would scale for fp8/int8 quant. |
| Workload reuse is overwhelmingly intra-session. | `supported` | Real reuse decomposition from w600 lmetric run: intra 93.2%, cross 5.7%, shared 1.1% (window_1_results/lmetric_reuse.json). Theoretical any-vs-intra ceiling gap 0.7 pp. | — | Trace-specific; ChatGPT-style workloads with long system prompts would shift toward shared-prefix. |
| Theoretical APC ceiling on w600 trace is 79.6% (intra) / 80.3% (any-session). | `supported` | window_1_results/apc_upper_w600.json from block-level trie walk on `hash_ids`. | — | Assumes infinite per-worker cache (no eviction); achieved values must be read as fraction of this ceiling. |
| Cache-aware LMetric leaves a measurable locality gap (22.7 pp). | `supported` | lmetric achieved 56.9% vs intra-session ceiling 79.6%; B3 sweep window_1_results/b3_policy_comparison.json. | — | sticky data shows the gap can be recovered by harder affinity. |
| Hybrid affinity (`unified`) breaks the locality-vs-latency tradeoff. | `supported` | unified APC 79.4% (97% of intra ceiling) AND TTFT p90 7.24 s (lmetric is 15.6 s). | — | unified concentrates a single very hot worker (engine_4 at 37.7 s p90); hotspot_index 3.35. |
| Same-worker prefill-decode interference is causal, not correlation. | `supported` | B2 microbench: different-worker control idx 0.92-1.02 across 32× prefill-size variation; same-worker TTFT idx scales 2.15× (2k) → 218× (65k). window_1_results/b2_sweep_summary.json. | — | Synthetic decode load (256-token prompts at 4 req/s) bounds the realism; production behavior is layered on top of B3. |
| The cost of same-worker prefill interference migrates from TPOT to TTFT as prefill size grows past the chunked-prefill horizon. | `supported` | B2 same-worker TPOT p90 idx peaks at 32k (7.89×) and *drops* at 65k (2.26×), while TTFT idx grows monotonically (94.6× → 218×) and TPOT p99 grows monotonically (59 → 169.5 ms). See window_1_results.md "TPOT idx peaks at 32k, not 65k". | — | SLO thresholds for TTFT and TPOT cannot be the same under PD-colo; this should be reflected in B4 SRR sweep design. |
| Hard session affinity (`sticky`) inflates same-worker prefill-decode interference. | `supported` | sticky interference_index 13.65 vs lmetric 6.53; sticky's slow-request breakdown 57% same-worker overlap vs lmetric 23%. | — | Confirms the B2 causal claim observed at the system level. |
| Heavy-tail sessions are a contributor to hot-spot but not the sole cause. | `supported` | Cap-8 trace (37% requests dropped) reduces hotspot_index only ~10% (2.253 → 2.020 after fixing the `joined_analysis.hotspot_index` median bug). | Run capped under unified to see whether unified's hotspot also persists. | Reviewer might counter that cap=8 is too soft; a stricter cap could be tried. |
| B3 saturated-replay latency gaps include an agentic dispatch-coupling feedback term, which is intentional and matches production. | `supported, framed as feature` | `replayer/replay.py:282-287` fires turn N+1 immediately when turn N is behind schedule (no human think-time). Under saturation, slow policies have longer mean session lifetime, more concurrent in-flight, higher worker pressure — so B3 latency gaps reflect "policy + feedback amplification", which is what a production operator switching policies on agentic workload experiences. See `analysis/characterization/agentic_dispatch_coupling.md`. | Run B4 open-loop Poisson at fixed λ to get the orthogonal "controlled-load" measurement; both are needed, not "B4 fixes B3". | Some reviewers will read "non-Poisson arrivals" as benchmark crime; the rebuttal is the agentic-vs-chat workload distinction. |
| SRR per policy under SLO is not yet measured. | `not_yet_supported` | B3 was driven by trace timestamps with strict session sequentiality; saturation is reached but not parameterized. | Run B4 with the A4 open-loop Poisson loadgen, per-class SLO, 5 policies × λ binary search. | Without B4 the paper cannot claim "policy X sustains higher load than Y". |
| Failure attribution near SRR boundary is not yet measured. | `not_yet_supported` | B5 protocol exists; no runs. | After B4, rerun each policy at 0.9× / 1.0× / 1.1× of its SRR_max with the same instrumentation, label slow requests. | The current `joined_analysis.label_slow_requests` is the labeler; needs SRR boundaries to point at. |