{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A support or data assistant repeats work across related customer requests.",
    "change": "Try the paper's memory change in a replay of past tasks, with separate memory per customer.",
    "baseline": "Your current memory pipeline plus a no-memory control using the same agent.",
    "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": "Task success, stale memories, retrieval misses, response latency, and all memory maintenance costs.",
    "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": "2609.15408",
  "title": "MarKey: Marginal Utility Guided Greedy Keyframe Selection for Long Video Understanding",
  "summary": "Replaces uniform frame sampling or per-frame relevance scoring that ignores subset redundancy, by greedily selecting frames via a utility combining query relevance, marginal coverage gain, and redundancy against already-selected frames. Costs are modest: it is training-free and adds an iterative scoring loop, mitigated by anchor-based coverage approximation and a bounded comparison window, though this still adds preprocessing latency over uniform sampling. Can fail when the tractable surrogate misestimates marginal utility, when anchor sets poorly represent video coverage, or on content where decisive evidence is not query-aligned, yielding missed frames despite the subset-aware objective.",
  "rationale": "It is a training-free, inference-time method compatible with a single 24 GB GPU or API-based MLLMs that improves long-video QA accuracy within a fixed visual-token budget, making it directly adoptable for production video-understanding workloads.",
  "technique": "Marginal-utility greedy keyframe selection for long video",
  "family": "memoria_e_contexto",
  "recommendation": "adotar",
  "published": "2026-09-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.15408",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Reports consistent improvement over existing training-free keyframe selection methods across six video-understanding benchmarks, with gains robust to MLLM backbone, model scale, and frame budget; no single multiplicative factor is stated in the abstract.",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
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      "source_page": null,
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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": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "selector_failed",
  "core_kind": "",
  "equations_fetched_at": "2026-10-07",
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}