{
  "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": "2610.17917",
  "title": "Batch-Paced: Fixed-Budget Batch Decoding in Discrete Diffusion Language Models",
  "summary": "Batch-Paced replaces per-sequence dLLM decoding schedules, which allocate compute independently per request, with a training-free joint allocation of a fixed decoding budget across a batch. It costs additional scheduling logic at the serving layer and optional quality trade-off when a decoding-depth allowance is used to shorten batch decode time, though reported decode time remains competitive. It fails if the serving stack runs autoregressive models rather than discrete diffusion LLMs, or at small batch sizes where the reported gains shrink.",
  "rationale": "The method is training-free and directly deployable in a serving stack, but its benefit is conditional on actually serving discrete diffusion LLMs at non-trivial batch sizes, which requires validation against the reader's workload.",
  "technique": "Budget-aware batch decoding for diffusion LLMs",
  "family": "serving_e_batching",
  "recommendation": "testar",
  "published": "2026-10-13",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.17917",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.17917",
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    },
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      "source_url": "https://arxiv.org/abs/2610.17917",
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    }
  ],
  "exposure_map": [
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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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  "open_questions": [],
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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": "",
  "equations_fetched_at": "",
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}