{
  "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": "2610.06843",
  "title": "Recursive Video In-Context Learning for Agentic Robot",
  "summary": "RV-ICL replaces stuffing demonstration videos or fixed keyframes into an agent's prompt with a training-free hierarchy of sub-events (keyframes, phases, moments, clips) exposed as read-only tools that the agent traverses on demand. The cost is the offline construction of the per-task event hierarchy plus extra tool-call turns during execution, and gains are reported only on LIBERO variants atop a specific frozen VLA backbone. It can fail if sub-event segmentation is inaccurate, if the agent navigates to irrelevant hierarchy levels and wastes context, or if the method does not transfer to real robots or non-LIBERO tasks.",
  "rationale": "The method is training-free and needs only one demonstration per task, which suits constrained infrastructure, but the reader must validate it on their own VLA stack and task distribution since evidence is limited to simulated LIBERO benchmarks with one backbone.",
  "technique": "Hierarchical demonstration video as navigable tool context",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06843",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 225,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.06843",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Raises success from 92.6% to 96.5% on LIBERO-PRO and from 86.7% to 95.8% on LIBERO-Plus, using one demonstration per task.",
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      "result": "",
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    }
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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    },
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      "dimension": "reproducibility",
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      "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": "",
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}