{
  "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": "2609.27286",
  "title": "Memory Control Signals Emerge Before Action in Long Horizon Agents",
  "summary": "Replaces fixed or heuristic context compression/retrieval policies with decisions guided by the model's own pre-action hidden states, combining state-guided compression with selective retrieval of historical evidence (PaMER+ adds step-level evidence selection). Costs include training or fitting probes on hidden states (which requires access to model internals, excluding closed third-party APIs), plus an external evidence store and added orchestration complexity, with modest per-step overhead. It can fail if probing signals do not transfer across models, tasks, or prompt distributions, if compression discards evidence that step-level selection later needs, or if gains shrink on tasks whose histories are short.",
  "rationale": "The approach is infrastructure-feasible for open-weights models on a single GPU and targets a real production cost driver, but the abstract offers no quantitative results or released artifacts, so workload-specific validation is required before adoption.",
  "technique": "Probing-guided pre-action memory control with evidence retrieval",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.27286",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.27286",
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    },
    {
      "claim_id": "claim-02",
      "statement": "PaMER substantially reduces context consumption while maintaining competitive task performance on WorkBuddyBench across baselines and backbones; no quantitative factor is reported in the abstract.",
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  ],
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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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": "",
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}