{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your inference service slows down when several customers send requests together.",
    "change": "Replay a recorded request trace with only the proposed serving policy changed.",
    "baseline": "Your current scheduler on the same model and hardware.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Requests meeting your latency target, throughput, failures, and cost per successful request.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.18112",
  "title": "Token Latency Fairness: Performance Isolation for Multi-Tenant LLM Serving",
  "summary": "FairInference replaces throughput-equalizing fair queueing and batching policies in multi-tenant LLM serving with a scheduler that enforces per-token deadlines, bounding delays from shared GPU compute and shared KV-cache memory. It costs additional scheduling complexity and per-token deadline bookkeeping, and it requires control over the serving stack rather than black-box API access. It can fail if the reader's workload is single-tenant or low-concurrency, where fairness guarantees are irrelevant and the scheduling overhead yields no benefit.",
  "rationale": "This is infrastructure for operators of multi-tenant GPU serving fleets, so a single-GPU or API-consuming practitioner gains nothing deployable today and should only track it as a potential capability of future serving frameworks.",
  "technique": "FairInference: per-token deadline scheduling for latency fairness",
  "family": "serving_e_batching",
  "recommendation": "observar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18112",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "FairInference replaces throughput-equalizing fair queueing and batching policies in multi-tenant LLM serving with a scheduler that enforces per-token deadlines, bounding delays from shared GPU compute and shared KV-cache memory. It costs additional scheduling complexity and per-token deadline bookkeeping, and it requires control over the serving stack rather than black-box API access. It can fail if the reader's workload is single-tenant or low-concurrency, where fairness guarantees are irrelevant and the scheduling overhead yields no benefit.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18112",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Claims to bound token-level latency spikes for well-behaved clients (delta-token fairness: multi-tenant latency within d + delta of isolated latency) and to improve overall throughput versus state-of-the-art serving systems, but no quantified factor is stated in the abstract.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18112",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "finding": "",
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      "limitation": "",
      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}