{
  "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": "2609.37626",
  "title": "SPLASH: Switching Parallel Layouts of Attention with Seamless Handoff for LLM Serving",
  "summary": "The method replaces serving engines that fix one attention parallel layout (tensor, data-parallel, or context parallelism) at launch with a system that switches layouts during request execution, reusing already-placed KV cache and weights and handing off at batch boundaries, plus a new Decoupled Ownership Parallelism layout offering 27-60% more KV capacity. The cost is substantial systems complexity: background state migration, a transition-aware scheduler, and a multi-GPU serving stack that the reader does not operate. It can fail when its layout decoupling assumption (few or no KV heads, as in MLA-style models) does not hold, when migration bandwidth contention erodes the near-free switch claim, or when load does not actually shift between layout regimes.",
  "rationale": "The technique presupposes a multi-GPU distributed serving deployment across B200/H200-class accelerators, which is outside the reader's single-GPU or API-based infrastructure, though the finding that optimal layout varies with concurrency and prompt length is worth monitoring as it may influence future versions of commodity serving engines.",
  "technique": "Runtime switching of attention parallelism layouts",
  "family": "serving_e_batching",
  "recommendation": "observar",
  "published": "2026-09-29",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.37626",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces serving engines that fix one attention parallel layout (tensor, data-parallel, or context parallelism) at launch with a system that switches layouts during request execution, reusing already-placed KV cache and weights and handing off at batch boundaries, plus a new Decoupled Ownership Parallelism layout offering 27-60% more KV capacity. The cost is substantial systems complexity: background state migration, a transition-aware scheduler, and a multi-GPU serving stack that the reader does not operate. It can fail when its layout decoupling assumption (few or no KV heads, as in MLA-style models) does not hold, when migration bandwidth contention erodes the near-free switch claim, or when load does not actually shift between layout regimes.",
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      "source_url": "https://arxiv.org/abs/2609.37626",
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      "statement": "SPLASH improves end-to-end serving throughput by 1.3-1.73x over fixed-layout deployments on B200 GPUs serving GLM-5.3, with median layout-switch overhead under 0.51% of a step.",
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      "dimension": "latency",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    {
      "dimension": "security",
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    {
      "dimension": "data_and_training",
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
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}