{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A product assistant retrieves plausible documents but gives unsupported answers.",
    "change": "Change one retrieval or ranking step while keeping the document snapshot fixed.",
    "baseline": "Your current retrieval pipeline with identical questions and answer model.",
    "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": "Supported answers, retrieval misses, correct abstentions, index cost, and response latency.",
    "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": "2610.30755",
  "title": "Is Complex Agentic Memory Always Needed? Naive RAG Is Stronger Than We Thought",
  "summary": "The method replaces complex agentic memory pipelines involving mutation, structural organization, and learned retrieval with dense retrieval and reranking directly over raw conversation history. Its cost is the standard retrieval and reranking infrastructure plus a reader LLM call, avoiding the engineering and maintenance burden of memory transformation pipelines. Failure modes include degraded performance when retrieval misses necessary evidence, poor scaling to extremely long histories within a fixed context window, and domain shift where reranking quality drops.",
  "rationale": "The paper provides direct benchmark evidence that a simple, well-understood retrieval pipeline matches or exceeds more complex alternatives on conversational memory tasks, making it a practical and lower-risk default for a constrained team.",
  "technique": "Naive RAG over raw conversation history",
  "family": "recuperacao_e_rag",
  "recommendation": "adotar",
  "published": "2026-10-22",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30755",
  "independent_implementations": 5,
  "total_implementations": 6,
  "stars": 152,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces complex agentic memory pipelines involving mutation, structural organization, and learned retrieval with dense retrieval and reranking directly over raw conversation history. Its cost is the standard retrieval and reranking infrastructure plus a reader LLM call, avoiding the engineering and maintenance burden of memory transformation pipelines. Failure modes include degraded performance when retrieval misses necessary evidence, poor scaling to extremely long histories within a fixed context window, and domain shift where reranking quality drops.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.30755",
      "source_page": null,
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    },
    {
      "claim_id": "claim-02",
      "statement": "Raw-RAG ranks first among compared memory systems in six of seven benchmark-model settings across LoCoMo, LongMemEval, MEME, and MemoryAgentBench, outperforming the strongest agentic memory system by 0.13 to 5.53 points.",
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      "source_url": "https://arxiv.org/abs/2610.30755",
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    }
  ],
  "exposure_map": [
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    },
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      "dimension": "latency",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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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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      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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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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}