{
  "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": "2609.24352",
  "title": "Few-Shot Demonstrations Elicit the Use of In-Context World Representations in LLMs",
  "summary": "Replaces zero-shot or single-world in-context observation formats with few-shot demonstrations drawn from multiple distinct environments sharing or varying latent structure. Costs only additional prompt tokens and the effort of assembling demonstrations from heterogeneous tasks or worlds, with no extra memory, training, or infrastructure. Can fail if available demonstrations do not actually span diverse underlying structures, if the task lacks a latent state the model must infer, or if longer prompts exceed context budgets without proportional benefit.",
  "rationale": "The technique is a prompt-level change requiring no training or extra hardware, but the abstract offers no quantified gains, so the reader should validate it on their own agentic or state-tracking workloads before adoption.",
  "technique": "Cross-world few-shot demonstrations for in-context modeling",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24352",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 215,
  "citations": 0,
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      "dimension": "compatibility",
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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