{
  "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": "2610.23266",
  "title": "Beyond Marginal Influence: Interaction-Aware Memory Selection for LLM Agents",
  "summary": "JointCore replaces singleton-record deletion and relevance-ranked retrieval with relevance-cost-ordered group deletion that tests blocks of records before individuals when selecting a minimal sufficient memory subset for LLM agents. Its cost is additional subset evaluations and a more complex deletion procedure, and the authors explicitly state it is a benchmark baseline on JointCoreBench rather than deployment-ready memory compression. Behavior is interface-dependent: physical deletion, UNKNOWN masks, PENDING sentinels, and counterfactual values produce different results, so sufficiency guarantees established under the deterministic primary interface may not transfer to a production memory store.",
  "rationale": "The method is validated only on a synthetic benchmark with exact oracles and is explicitly not established as deployment-ready, so it merits monitoring rather than adoption or small-scale testing in production memory systems.",
  "technique": "Interaction-aware group-deletion memory selection",
  "family": "memoria_e_contexto",
  "recommendation": "observar",
  "published": "2026-10-16",
  "as_of": "2026-10-19",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.23266",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "JointCore replaces singleton-record deletion and relevance-ranked retrieval with relevance-cost-ordered group deletion that tests blocks of records before individuals when selecting a minimal sufficient memory subset for LLM agents. Its cost is additional subset evaluations and a more complex deletion procedure, and the authors explicitly state it is a benchmark baseline on JointCoreBench rather than deployment-ready memory compression. Behavior is interface-dependent: physical deletion, UNKNOWN masks, PENDING sentinels, and counterfactual values produce different results, so sufficiency guarantees established under the deterministic primary interface may not transfer to a production memory store.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.23266",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "JointCore uses an unweighted mean of 10.28 model queries per task versus 16.00 for cost-aware singleton deletion, with 50.6% versus 43.1% minimum-cost sufficient-set recovery across four 12B-70B model families.",
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  ],
  "exposure_map": [
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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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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      "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}