{
  "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": "2609.31255",
  "title": "PIA: A Personal Intelligence Agent Turning Health Conversations into Records and Records into Understanding",
  "summary": "PIA replaces the standard summarize-embed-retrieve agent memory (salient snippets plus top-k similarity) with typed clinical records produced by four controls — extraction, memory, retrieval, and understanding — backed by pluggable domain modules such as schemas, a medical alias dictionary, a knowledge graph, and temporal rules. The cost is substantial schema and rule engineering per domain, an additional agent that autonomously decides reads and writes, and heavier context synthesis (snapshot and trajectory-with-causality layers) that increases prompt size and latency. What can fail: temporal expressions like 'since last week' require explicit resolution rather than model discretion, self-reported data are missing not at random and can bias synthesized understanding, question phrasing governs synthesis quality, and roughly a third of candidate causal links are structural noise that must be filtered by rules.",
  "rationale": "The architecture is directly implementable on constrained infrastructure using API models and a database, but the reported failure modes and lack of quantified gains mean the reader should validate the extraction and temporal-rule quality on their own health workload before production adoption.",
  "technique": "Typed record-based memory harness for health agents",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-25",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.31255",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "PIA replaces the standard summarize-embed-retrieve agent memory (salient snippets plus top-k similarity) with typed clinical records produced by four controls — extraction, memory, retrieval, and understanding — backed by pluggable domain modules such as schemas, a medical alias dictionary, a knowledge graph, and temporal rules. The cost is substantial schema and rule engineering per domain, an additional agent that autonomously decides reads and writes, and heavier context synthesis (snapshot and trajectory-with-causality layers) that increases prompt size and latency. What can fail: temporal expressions like 'since last week' require explicit resolution rather than model discretion, self-reported data are missing not at random and can bias synthesized understanding, question phrasing governs synthesis quality, and roughly a third of candidate causal links are structural noise that must be filtered by rules.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.31255",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": ""
}