{
  "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": "2610.25456",
  "title": "System X: An LLM Decision Model That Replies from Its Own KV Cache",
  "summary": "The method replaces a separate full-prompt second LLM call for text replies with continuation from the decision model's existing KV cache after a Jev-style option readout. It costs a LoRA fine-tuning pipeline, routing-data curation, and added serving complexity, and gains remain workload-dependent rather than a fixed multiplicative factor. Failures include state conflicts, unsupported reply details, possible instruction-following degradation from added support data, and reply-judge results confounded by prompt and generation-limit differences.",
  "rationale": "For a 24 GB or API-constrained team, the idea is practical to prototype because it reuses a small LoRA-adapted model and can reduce latency, but the mixed quality evidence and remaining failure modes justify a controlled pilot rather than production adoption.",
  "technique": "KV-cache reuse for decision-to-reply LLM routing",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-10-18",
  "as_of": "2026-10-25",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.25456",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces a separate full-prompt second LLM call for text replies with continuation from the decision model's existing KV cache after a Jev-style option readout. It costs a LoRA fine-tuning pipeline, routing-data curation, and added serving complexity, and gains remain workload-dependent rather than a fixed multiplicative factor. Failures include state conflicts, unsupported reply details, possible instruction-following degradation from added support data, and reply-judge results confounded by prompt and generation-limit differences.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.25456",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Cache reuse saves 15-37% latency on Apple M4 Pro and 17-39% on Qwen3-1.7B; calibration error falls from 0.292 to 0.169; v4 JevBench accuracy rises 55.0% to 61.0% (p=0.04), with no significant IFEval change.",
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      "source_url": "https://arxiv.org/abs/2610.25456",
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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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      "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": "",
  "exposures_reviewed_at": "",
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}