{
  "_about": "fak position rows for the 'agent' group. dim_id links each to a _taxonomy.json dimension.",
  "rows": [
    {
      "id": "kv-aware-routing-locality",
      "dim_id": "kv-aware-routing-locality",
      "axis": "Cache-aware request routing across replicas (route to the worker that already holds the prefix)",
      "category": "request-routing",
      "competitor": "NVIDIA Dynamo (Smart Router); SGLang router; vLLM production router",
      "competitor_class": "sota",
      "competitor_value": null,
      "competitor_range": [
        50,
        99
      ],
      "competitor_source": "Baseten-on-Dynamo 0.89 cross-replica cache hit across 4 replicas (https://www.baseten.co/blog/how-baseten-achieved-2x-faster-inference-with-nvidia-dynamo/, 2025-01); SGLang RadixAttention published 50-99% hit-rate band (arXiv:2312.07104, NeurIPS 2024); NVIDIA Dynamo Smart Router (https://developer.nvidia.com/blog/introducing-nvidia-dynamo-a-low-latency-distributed-inference-framework-for-scaling-reasoning-ai-models/, 2025-03-18).",
      "fak_value": 93.97,
      "unit": "% cross-replica prefix-KV cache-hit",
      "higher_is_better": true,
      "verdict": "parity",
      "status": "shipped",
      "fak_commit": "",
      "fak_artifact": "experiments/kv-fleet-routing/kv-fleet-routing-hitrate-20260627.json",
      "fak_doc": "internal/gateway/kv_fleet_routing.go (MeasureKVAwareFleetRouting) witnessed by TestKVAwareFleetRoutingHitRate; artifact kv-fleet-routing-hitrate-20260627.json (verify kv_aware_locality.hit_rate=0.9396551724137931). The cross-instance analogue of the on-instance row 'cache-aware-scheduling-routing' (FCFS 62.1% -> 86.7%).",
      "apples_to_apples": false,
      "comparison_note": "fak now ships the multi-replica router this row asked for: gateway.FleetCacheRouter steers a request to the instance already holding its prefix KV, homing a cold prefix on the least-loaded instance (the Dynamo Smart Router / SGLang cache-aware router pattern), composed from the on-instance residency signal + the cross-agent coherence bus (vdso.Revoke). WITNESSED number: 93.97% cross-instance prefix-KV hit rate on a Zipf-skewed shared-prefix agent fleet (4 instances x 8 prefixes vs 28 families), a +10.4% lift over the cache-blind round-robin that gateway.ReplicaRouter.pick does today (85.13%). Sits inside the published cross-replica cache-hit band (50-99%) and clears Baseten-on-Dynamo's 0.89 -> parity, the fleet analogue of the on-instance 86.7%. FENCE: this is the routing-DECISION hit rate from a deterministic host-free simulation of the placement policy (no GPUs) -> apples_to_apples=false (same metric class as Baseten's live cross-replica hit, different apparatus). The downstream wall-clock win those hits produce on real GPUs (Baseten -50% TTFT / +62% output tok/s; GORGO 2.5x median TTFT) is host-gated and explicitly NOT claimed by this number.",
      "measured_on": "2026-06-27",
      "verify": {
        "field": "kv_aware_locality.hit_rate",
        "expect": 0.9396551724137931,
        "tol": 1e-06
      }
    },
    {
      "id": "fleet-serving-50x5",
      "dim_id": "cross-agent-fleet-serving",
      "axis": "Cross-agent fleet serving time (N agents × T turns): work eliminated by shared-prefix fusion",
      "category": "agent-fleet",
      "competitor": "Tuned warm per-agent KV cache (the SGLang / vLLM / OpenAI-prompt-caching floor)",
      "competitor_class": "sota",
      "competitor_value": 78.0,
      "competitor_source": "vLLM Automatic Prefix Caching (docs); SGLang RadixAttention (Zheng et al., arXiv:2312.07104); OpenAI prompt caching — the same warm-KV reuse discipline. Baseline arm is fak's own warm-KV mode, kernel held constant.",
      "fak_value": 19.0,
      "unit": "min",
      "higher_is_better": false,
      "fak_commit": "2bbda6f",
      "fak_artifact": "experiments/session/headline-qwen-50x5.json",
      "fak_doc": "BENCHMARK-AUTHORITY.md",
      "apples_to_apples": true,
      "comparison_note": "Both arms run live on ONE shared kernel held constant, so the 4.1× isolates cross-agent prefix fusion — work ELIMINATED, not a faster kernel. A production vLLM/SGLang would shrink both arms' absolute minutes but is expected to leave the reuse ratio ~intact (κ·N: uniform per-token speedup scales both arms).",
      "measured_on": "2026-06-19",
      "verify": {
        "field": "cells[0].net_value_add_vs_tuned",
        "expect": 4.124566205185288,
        "tol": 1e-06
      },
      "verdict": "lead",
      "status": "shipped"
    },
    {
      "id": "marginal-vs-warm-cache",
      "dim_id": "marginal-reuse-over-warm-cache",
      "axis": "Marginal value of cross-agent prefix fusion ON TOP of an already-warm per-agent cache",
      "category": "agent-fleet",
      "competitor": "Tuned warm per-agent KV cache, already hot (B)",
      "competitor_class": "tuned",
      "competitor_value": 1.0,
      "competitor_source": "fak SESSION-VALUE-STACK-RESULTS.md B/C column (2.70× @T8, 2.41× @T16); the baseline is fak's own warm-KV arm B — the most conservative SOTA point.",
      "fak_value": 2.4,
      "unit": "×",
      "higher_is_better": true,
      "fak_commit": "92896a4",
      "fak_doc": "SESSION-VALUE-STACK-RESULTS.md · BENCHMARK-AUTHORITY.md",
      "apples_to_apples": true,
      "comparison_note": "Cross-agent prefix fusion ON TOP of an already-warm per-agent cache — the honest marginal few-fold (2.4–2.7×, conservative end shown), NOT the 60× headline vs naive. FENCE: the 'tuned' baseline is fak's OWN warm-KV arm B (bit-exact, kernel held constant), NOT a live SGLang/vLLM process — a real competing engine also fuses a shared prefix once, so the marginal-vs-a-live-tuned-process number is unmeasured (see row 'marginal-vs-tuned-process').",
      "measured_on": "2026-06-19",
      "verdict": "lead",
      "status": "shipped"
    },
    {
      "id": "marginal-vs-tuned-process",
      "dim_id": "marginal-reuse-vs-live-engine",
      "axis": "Cross-agent reuse marginal value vs a LIVE tuned shared-prefix engine (head-to-head)",
      "category": "agent-fleet",
      "competitor": "SGLang RadixAttention / vLLM Automatic Prefix Caching (actual competing processes)",
      "competitor_class": "sota",
      "competitor_value": null,
      "competitor_source": "HERO-BENCHMARK-2026-06-21.md (baseline-is-fak's-own-arm fence); WebVoyager baseline-stratification (marginal-vs-tuned ~1.0–1.10×); vLLM APC / SGLang RadixAttention",
      "fak_value": null,
      "unit": "× over a live tuned engine",
      "higher_is_better": true,
      "fak_doc": "HERO-BENCHMARK-2026-06-21.md · docs/webbench-real-measurements-summary.md",
      "apples_to_apples": true,
      "comparison_note": "Every committed fleet multiplier (4.1×, 60.3×, 139×) is measured vs NAIVE re-prefill or fak's OWN kernel held constant in warm-KV mode — NOT a head-to-head against a live SGLang/vLLM process, which ALSO fuses a shared prefix once. The honest marginal-vs-a-real-tuned-process number is UNMEASURED; the repo's WebVoyager baseline-stratification record puts the marginal-vs-tuned win at only ~1.0–1.10× on that workload. Naming this gap stops the 4.1× from being read as a vs-live-competitor result.",
      "measured_on": "2026-06-23",
      "verdict": "no-claim",
      "status": "projected"
    },
    {
      "id": "model-routing-granularity",
      "dim_id": "model-routing-granularity",
      "axis": "Model routing granularity: per-aspect + first-class ensemble routing vs whole-request single-model selection",
      "category": "model-routing",
      "competitor": "RouteLLM (LMSYS); Martian; NotDiamond; Unify.ai; OpenRouter (+ Fusion); Portkey; LiteLLM Router",
      "competitor_class": "sota",
      "competitor_value": null,
      "competitor_source": "docs/model-routing.md — survey of 2025-2026 routers/gateways (2026-06); RouteLLM (lm-sys/RouteLLM), OpenRouter Fusion, Portkey, LiteLLM Router docs",
      "fak_value": null,
      "unit": "qualitative (per-aspect routing + configurable ensemble reduction, vs whole-request single-model)",
      "higher_is_better": true,
      "verdict": "lead",
      "status": "shipped",
      "qualitative": true,
      "fak_commit": "2298421",
      "fak_artifact": "",
      "fak_doc": "docs/model-routing.md (survey + status); BENCHMARK-AUTHORITY.md (fak routebench)",
      "apples_to_apples": true,
      "comparison_note": "To our knowledge / among surveyed 2025-2026 routers and gateways (RouteLLM, Martian, NotDiamond, Unify, OpenRouter, Portkey, LiteLLM Router), every one routes the WHOLE request to ONE model and the only shipped model ensemble is OpenRouter Fusion — a fixed parallel-synthesize recipe, not a configurable reduction. fak routes at the ASPECT level (request | tool_call | query | step), so one request can send different aspects to different models, with first-class ENSEMBLES folded by a configurable reduction (first | vote | best_of | all_reduce | concat), expressed as one deterministic, verifiable policy. The decision spine (Route + Combine) and the offline benchmark (fak routebench) are SHIPPED (commit 2298421, witnessed by go test), so this is a CATEGORICAL capability lead on routing granularity and ensemble expressiveness — NOT a benchmarked speed/quality win. Any '10x' is a target to be measured, never an inferred or borrowed number; the live multi-model dispatch that executes a decision on real engines is STUB, so the end-to-end competitive latency/cost/quality delta is unmeasured.",
      "measured_on": "2026-06-24"
    }
  ]
}