{
  "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": "2610.21165",
  "title": "From Long Context to Selective Memory:A Survey of Memory-Centric Compression for Large Language Models",
  "summary": "This survey reframes the isolated practices of prompt compression, KV-cache management, and retrieval augmentation as a unified memory-management problem, distinguishing compute-active from compute-inactive memory and modeling operations (offloading, recall, materialization, eviction, forgetting) as budgeted state transitions rather than proposing a deployable method. It imposes no runtime cost itself, but adopting its lifecycle-aware design perspective adds architectural complexity to systems that must decide which information enters costly inference, when, and in what form. Because it is a taxonomy and cost model without validated implementations, applying its framework prematurely could yield over-engineered memory hierarchies whose eviction and forgetting policies silently discard information needed for task correctness.",
  "rationale": "As a survey and conceptual framework with no quantified gains or concrete implementation, it is valuable for orienting long-context system design but does not itself provide a method the reader can adopt or benchmark on constrained infrastructure.",
  "technique": "Memory-centric compression framework for LLMs",
  "family": "memoria_e_contexto",
  "recommendation": "observar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21165",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 128,
  "citations": 0,
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      "claim_id": "claim-01",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21165",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "finding": "",
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      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}