{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Long customer conversations exhaust memory or limit concurrent requests.",
    "change": "Change only the attention cache policy, after checking model and runtime support.",
    "baseline": "The same model with its ordinary attention cache.",
    "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": "Peak cache memory, long-context answer quality, first-token latency, and requests completed.",
    "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": "2609.26796",
  "title": "Flash-dLLM: IO-Aware KV Caching and Parallel Decoding for Fast, Memory-Efficient Diffusion LLMs",
  "summary": "Flash-dLLM replaces separate, uncoordinated KV-caching and parallel-decoding accelerations for diffusion LLMs with a fused IO-aware KV-cache kernel plus a self-draft-and-verify decoding loop that needs no auxiliary model. It is training-free, but costs custom CUDA kernel integration and is tied to dLLM inference stacks rather than standard autoregressive serving frameworks. It can fail if the reader's workloads run on autoregressive models (where the technique does not apply), if the fused kernel is not maintained for their hardware or model version, or if draft acceptance rates drop on domains unlike the evaluated math and code benchmarks.",
  "rationale": "The method is specific to diffusion LLMs, which are not yet a standard production option on a single 24 GB GPU or via third-party APIs, so it is worth tracking but not yet adoptable for typical constrained-infrastructure deployments.",
  "technique": "IO-aware KV caching with self-draft parallel decoding for dLLMs",
  "family": "cache_kv",
  "recommendation": "observar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26796",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Reports 5.1x and 11.0x speedups over the strongest prior baseline (Elastic-Cache) on GSM8K and HumanEval respectively, with improved memory efficiency and no quality loss claimed.",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    {
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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": "",
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