{
  "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": "2609.27233",
  "title": "Distilling Sequential Computation in Transformer Language Models",
  "summary": "The method replaces multi-token spans in the prompt and stored KV cache entries with single surrogate embeddings produced by a lightweight merge module, applied to pretrained models without architectural changes or fine-tuning, using a rollback mechanism during decoding. It costs training and maintaining the merge module (plus optional lightweight adaptation for better trade-offs) and introduces some accuracy degradation that grows with the compression rate. It can fail on workloads where predictable-span assumptions break down, such as code, exact-copy retrieval, or numeric content, where merging tokens loses functionally critical information.",
  "rationale": "It fits a single 24 GB GPU and requires no model retraining, but the accuracy-compression trade-off must be validated on the reader's specific tasks before production use.",
  "technique": "On-the-fly token-span merging for prompt and KV cache compression",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.27233",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
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      "dimension": "compatibility",
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      "dimension": "security",
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      "dimension": "reproducibility",
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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