{
  "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": "2609.26300",
  "title": "CompKV: Compensation-Aware KV Selection for Long-Context LLM Inference",
  "summary": "CompKV replaces decoupled select-then-compensate sparse attention by selecting KV blocks according to the residual they would leave under block-mean compensation, using block attention mass and within-block logit variation as the criterion. It costs additional block-level statistics computation and an asynchronous implementation, adding integration complexity relative to off-the-shelf sparse attention kernels. It can fail on workloads where block statistics poorly approximate the true compensation residual, on short contexts where selection overhead dominates, and because reported gains are benchmark-specific and require reimplementation since no production-grade serving integration is established.",
  "rationale": "The method targets exactly the long-context KV bottleneck a single-GPU practitioner faces, but its gains depend on workload and implementation maturity, so it warrants a controlled evaluation on the reader's own inference traffic before production adoption.",
  "technique": "Compensation-aware block KV selection for sparse attention",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26300",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.26300",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2609.26300",
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    }
  ],
  "exposure_map": [
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      "dimension": "latency",
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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": [],
  "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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}