{
  "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": "2610.10778",
  "title": "MemoWM: How World Models Change What Agents Need to Remember",
  "summary": "It replaces storing complete per-experience records in agent memory with a task-aware allocation rule that retains only information whose downstream value exceeds what a shared world model can predict and reconstruct. The cost is maintaining and querying a world model (parameter storage and inference overhead per memory write/read) plus an allocation scoring step, and total storage only improves if the world-model capacity stays below the recurring storage it saves. It can fail when the world model mispredicts task-relevant content, causing reconstruction errors on omitted details; quality therefore degrades on workloads whose regularities the world model has not captured.",
  "rationale": "The storage reduction is meaningful for memory-heavy agents on constrained infrastructure, but the framework depends on a trained world model whose reconstruction fidelity and capacity trade-off must be validated on the reader's specific interaction distribution before production adoption.",
  "technique": "World-model-conditioned memory allocation for agents",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.10778",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "statement": "MemoWM reduces average experience-specific storage by 53.9% relative to MIRIX (the most storage-efficient baseline) while achieving 42.42% average answer accuracy, 2.62 points above the strongest baseline, across five long-term agent-memory benchmarks.",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "dimension": "security",
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  "risks": [],
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
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  "equations_status": "not_fetched",
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