{
  "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": "2609.18935",
  "title": "Long-Lived Characters, Local Inference: Incremental Memory Maintenance for Game NPCs",
  "summary": "The method replaces full prefix recomputation when an NPC's memory changes: it surgically removes superseded KV entries, computes replacement records at the true sequence tail, and reuses the continuing recurrent state and untouched KV instead of re-encoding the entire memory text. The cost is implementation complexity tied to a specific quantized Qwen hybrid recurrent-attention architecture, plus the risk that incorrect placement weakens query-conditioned memory selection, as shown by the block-composition and slot-preserving ablations. After adoption it can fail silently on semantics rather than attention statistics: wrong update placement reproduced a double-subtraction error in ownership tracking, which can corrupt deterministic game rules that consume the dialogue output.",
  "rationale": "The approach addresses a real cost for locally deployed long-lived agents, but evidence comes from a single model family and scripted eight-round replays, so production value depends on validation against the reader's own model stack and dialogue workloads.",
  "technique": "Incremental KV and recurrent-state memory maintenance",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18935",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18935",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "compatibility",
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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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    },
    {
      "dimension": "reproducibility",
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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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}