{
  "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": "2610.12338",
  "title": "VFold: Symmetry-Aware Cross-Layer Value Cache Compression",
  "summary": "The method replaces the full per-layer value cache with a symmetry-aware merging scheme that shares or merges value states across layers, without modifying the model architecture or decoding procedure. Its cost is the additional merging logic and a potential quality risk that the paper claims is minimal; it is designed to stack with existing KV quantization and key pruning rather than replace them. It can fail if the claimed cross-layer symmetry does not hold for the reader's specific models or tasks, degrading output quality at aggressive compression ratios, and the abstract provides no benchmark numbers to confirm the degradation is truly negligible.",
  "rationale": "The technique targets exactly the reader's constraint (KV memory at long context on a single GPU) and requires no retraining or architectural change, but the absence of quantified results in the abstract means quality impact must be validated on the reader's workload before production adoption.",
  "technique": "Cross-layer value cache merging (VFold)",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.12338",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 122,
  "citations": null,
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      "dimension": "compatibility",
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      "dimension": "security",
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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