{
  "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": "2610.12668",
  "title": "Recurrent Harness for Agentic Continual Learning",
  "summary": "It replaces forward-only memory/reflection harnesses (and replay- or validation-based harness optimization) with a strictly online scheme that, after each streamed instance, revises previously retrieved reflections using the resulting trajectory as backward feedback. The cost is extra inference calls per instance for revision, added harness complexity, and growth/maintenance of a textual state, all feasible on a single 24 GB GPU or via APIs. It can fail if revisions propagate errors from noisy trajectories, if the workload lacks distribution shift or long interaction streams where continual adaptation matters, or if revision quality depends on backbone strength not matched in the reader's deployment.",
  "rationale": "The method is API- and single-GPU-compatible and addresses a real production concern (online adaptation without replay data), but the abstract reports only unquantified consistent improvements, so the reader should validate on their own streaming workload before adoption.",
  "technique": "Backward revision of textual agent memory",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.12668",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 14,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.12668",
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    },
    {
      "claim_id": "claim-02",
      "statement": "ReHarness 'consistently outperforms strong raw-context baselines, memory- and reflection-based methods, and prompt optimization approaches' across three continual learning benchmarks and three backbones; no quantitative margin is stated in the abstract.",
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    }
  ],
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      "dimension": "latency",
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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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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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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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}