{
  "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": "2609.31415",
  "title": "Evaluating the accuracy of KV cache reuse techniques",
  "summary": "This is an evaluation methodology plus the Boxoffice dataset generator, intended to replace the current practice of trusting published accuracy figures for position-independent KV cache reuse in RAG, which the authors show are often inflated by flawed measurements and unchallenging datasets. It costs engineering time to integrate the measurement protocol and generate synthetic evaluation datasets, and it provides no latency or memory benefit by itself. It can fail to transfer if the generated reuse patterns do not match the reader's actual chunk distribution, prompt structure, or model, and a negative result may force abandoning KV reuse and accepting full prefill latency.",
  "rationale": "Before adopting chunk-level KV cache reuse to cut RAG latency on a single-GPU setup, the reader should validate accuracy on their own workload with a sound methodology, since the paper shows standard evaluations overstate reuse quality.",
  "technique": "Rigorous evaluation of position-independent KV cache reuse",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.31415",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "dimension": "security",
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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": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}