{
  "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": "2610.18014",
  "title": "LatentRemedy: Efficient Remedy For Non-Prefix KV Cache Reuse in Multi-Agent Systems",
  "summary": "It replaces selective recomputation and offset-based calibration for recovering quality when reusing non-prefix KV cache blocks across agents with conflicting tasks, substituting recomputation of the shared block with a few MLP-generated latent tokens inserted after the reused block. The cost is training a small MLP via full-prefill distillation per model, plus extra inference-time compute and engineering to insert latent tokens into the serving path. It can fail outside multi-agent workloads with genuinely shared context blocks, if the distillation training does not transfer to a different base model or task distribution, or if the serving stack cannot accommodate inserted latent tokens without modification.",
  "rationale": "The method targets a narrow but real failure mode of cross-agent KV reuse and is deployable on a single GPU since it trains only a small MLP, but its benefit is workload-specific and requires integration and training effort, so small-scale validation is warranted before adoption.",
  "technique": "Latent token calibration for non-prefix KV cache reuse",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-10-13",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.18014",
  "independent_implementations": 0,
  "total_implementations": 2,
  "stars": 12,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces selective recomputation and offset-based calibration for recovering quality when reusing non-prefix KV cache blocks across agents with conflicting tasks, substituting recomputation of the shared block with a few MLP-generated latent tokens inserted after the reused block. The cost is training a small MLP via full-prefill distillation per model, plus extra inference-time compute and engineering to insert latent tokens into the serving path. It can fail outside multi-agent workloads with genuinely shared context blocks, if the distillation training does not transfer to a different base model or task distribution, or if the serving stack cannot accommodate inserted latent tokens without modification.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.18014",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Direct non-prefix KV reuse can degrade model performance by up to 90% on conflicting tasks; LatentRemedy consistently outperforms selective-recomputation and offset-based baselines while keeping TTFT close to direct reuse as cache size grows (no single quantified factor given).",
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      "result": "",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.18014",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": "",
  "exposures_source_sha256": ""
}