{
  "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": "2610.18654",
  "title": "Kwai Summary Attention V2 Technical Report",
  "summary": "KSA V2 replaces rigid same-window summary-token interleaving with summary tokens that act both as KV memory and sparse indexers, allowing per-layer compression windows and cheaper full-attention layers. It costs architectural modification and training from scratch or substantial fine-tuning, plus added serving complexity in the KV cache path, with unverified quality at long contexts. It can fail through degraded recall of distant exact content under aggressive per-layer compression and through implementation mismatch with standard inference stacks.",
  "rationale": "Because it requires training a custom attention architecture and custom KV cache handling, a team with one GPU or API-only access cannot adopt it now, but the dual-role summary token idea is worth tracking for future open-weight releases.",
  "technique": "Hybrid summary-token attention with per-layer compression (KSA V2)",
  "family": "cache_kv",
  "recommendation": "observar",
  "published": "2026-10-13",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.18654",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "KSA V2 replaces rigid same-window summary-token interleaving with summary tokens that act both as KV memory and sparse indexers, allowing per-layer compression windows and cheaper full-attention layers. It costs architectural modification and training from scratch or substantial fine-tuning, plus added serving complexity in the KV cache path, with unverified quality at long contexts. It can fail through degraded recall of distant exact content under aggressive per-layer compression and through implementation mismatch with standard inference stacks.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.18654",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "The abstract claims more flexible KV cache compression and reduced computation in full-attention layers, but provides no quantified factor.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.18654",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
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      "dimension": "compute",
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      "finding": "",
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": ""
}