{
  "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": "2609.27606",
  "title": "State-Grounded Conditioning: Wrapping User-Facing LLM Agents Where Direction Depends on Live State",
  "summary": "The method replaces monolithic prompt-based state injection and generic tool-use agent loops with explicit Perception, Grounding, and Interaction wrappers that externalise state-dependent control into rule kernels over structured state slices. It costs engineering effort to define state slices, write and maintain rule kernels, and wire conditioning dependencies, with latency and accuracy gains reported on a single narrow benchmark. It can fail when rule kernels drift out of sync with evolving application state schemas, when the three state slices are not actually orthogonal for a new domain, or when the 200-session in-game coaching results do not transfer to other agent workloads.",
  "rationale": "The wrapper pattern is implementable with a single GPU or API-backed model and addresses a real production failure mode, but the evidence comes from one domain-specific benchmark with no independent per-wrapper ablation, so adoption should follow workload-specific validation.",
  "technique": "State-grounded conditioning via rule-kernel wrappers",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.27606",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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    {
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      "statement": "Perception wrapper holds mean first-token latency at 1.5s vs 6.1s for a PE-Agent baseline; enabling all three wrappers lifts turn-level grounded accuracy from 61.1%/69.8% to 96.7% and session-level grounded accuracy from 20.0%/26.5% to 83.5%, with grounding-failure incidents down ~78%.",
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  "exposure_map": [
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      "dimension": "compute",
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      "dimension": "latency",
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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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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}