{
  "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": "2610.08268",
  "title": "DySCo: Dynamic Sharding for Collaborative Edge-Cloud LLM Inference with Depth-Synchronized Batching",
  "summary": "DySCo replaces conventional cloud-only batching (FIFO, exact-match, or round-robin scheduling of split-inference requests) with a runtime that keeps KV caches on edge devices, executes arbitrary contiguous layer ranges from resident shards without weight reloads, and batches the common cloud-side suffix of requests cut at heterogeneous depths. The cost is a custom model-aware executor, edge-resident KV cache memory, and scheduling complexity that only pays off at meaningful concurrency with mixed split points. It can fail when concurrency is low (no batching benefit), when edge devices are too weak or links too slow for split inference to beat direct API calls, and when the engineering effort exceeds the latency budget of a small team.",
  "rationale": "The method targets multi-edge serving fleets and split inference infrastructure that a single-GPU practitioner does not operate, so it is relevant to track but not deployable or testable under the stated constraints.",
  "technique": "Depth-synchronized batching for split edge-cloud inference",
  "family": "serving_e_batching",
  "recommendation": "observar",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.08268",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DySCo replaces conventional cloud-only batching (FIFO, exact-match, or round-robin scheduling of split-inference requests) with a runtime that keeps KV caches on edge devices, executes arbitrary contiguous layer ranges from resident shards without weight reloads, and batches the common cloud-side suffix of requests cut at heterogeneous depths. The cost is a custom model-aware executor, edge-resident KV cache memory, and scheduling complexity that only pays off at meaningful concurrency with mixed split points. It can fail when concurrency is low (no batching benefit), when edge devices are too weak or links too slow for split inference to beat direct API calls, and when the engineering effort exceeds the latency budget of a small team.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.08268",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "At average concurrency of eight, depth-synchronized batching improves throughput by 275% over FIFO, 48% over exact-match batching, and 79% over round-robin interleaving, while reducing mean per-session latency; idle gaps add up to 25 ms of extra cloud-side suffix latency per decoding step.",
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      "source_url": "https://arxiv.org/abs/2610.08268",
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      "source_excerpt": ""
    }
  ],
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
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      "next_check": "",
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      "source_page": null,
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    }
  ],
  "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": "",
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}