{
  "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": "2609.26763",
  "title": "SARA: SLO-Aware Resource Allocation for Disaggregated Agentic LLM Services",
  "summary": "It replaces hardware profiling, configuration enumeration, and heuristic scheduling for disaggregated prefill/KV-transfer/decode serving with queueing models plus an SLO- and cost-constrained allocator. It costs a disaggregated serving stack, stage-level workload and hardware parameters, quantile SLO definitions, and ongoing model calibration rather than a simple single-server deployment. It can fail when bursty agentic traffic, network contention, model changes, or HBM/compute behavior violate the M/G/k, M/G/1, and birth-death assumptions, and its allocation knobs are largely absent on one 24 GB GPU or third-party APIs.",
  "rationale": "The framework is designed for multi-stage disaggregated clusters, so its measured goodput benefit is not directly actionable on a single 24 GB GPU or API-only budget despite useful SLO-first capacity reasoning.",
  "technique": "SLO-aware disaggregated serving allocation",
  "family": "serving_e_batching",
  "recommendation": "observar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26763",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces hardware profiling, configuration enumeration, and heuristic scheduling for disaggregated prefill/KV-transfer/decode serving with queueing models plus an SLO- and cost-constrained allocator. It costs a disaggregated serving stack, stage-level workload and hardware parameters, quantile SLO definitions, and ongoing model calibration rather than a simple single-server deployment. It can fail when bursty agentic traffic, network contention, model changes, or HBM/compute behavior violate the M/G/k, M/G/1, and birth-death assumptions, and its allocation knobs are largely absent on one 24 GB GPU or third-party APIs.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.26763",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "The paper reports mean stage-wise SLO prediction errors below 5% and 26.6% average goodput improvement over state-of-the-art baselines under the same deployment cost.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.26763",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "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": ""
}