{
  "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": "2610.06286",
  "title": "DeferKV: Rethinking Eviction Timing for One-Shot KV Cache Compression",
  "summary": "DeferKV replaces the standard one-shot KV compression decision made immediately after prefill with an eviction decision deferred to the first real decoding step, combining prompt-side and decode-side attention signals. It costs no training, no draft model, and no future-query prediction module, requiring only retention of the full KV cache through the first decode step plus a modest change to the inference pipeline. It can fail if a single early decode query is unrepresentative of later attention, if the workload's bottleneck is peak prefill memory rather than steady-state decode cache, or if the serving framework does not expose eviction timing at the prefill-decode boundary.",
  "rationale": "The method is training-free and directly applicable to long-context serving on a single 24 GB GPU, but the unquantified quality gain and dependence on representative early decode queries justify workload-specific validation before adoption.",
  "technique": "Deferred one-shot KV cache eviction",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06286",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DeferKV replaces the standard one-shot KV compression decision made immediately after prefill with an eviction decision deferred to the first real decoding step, combining prompt-side and decode-side attention signals. It costs no training, no draft model, and no future-query prediction module, requiring only retention of the full KV cache through the first decode step plus a modest change to the inference pipeline. It can fail if a single early decode query is unrepresentative of later attention, if the workload's bottleneck is peak prefill memory rather than steady-state decode cache, or if the serving framework does not expose eviction timing at the prefill-decode boundary.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06286",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Consistently improves model performance under KV cache compression on LongBench, RULER, and Needle-in-a-Haystack while maintaining low inference latency; no multiplicative factor stated.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.06286",
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      "finding": "",
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      "dimension": "compute",
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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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": "",
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}