{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time budget.",
    "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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2609.17331",
  "title": "Self-Emergence Agent Architecture:Behavior-Inertia HMM, Reflexive Metacognition,and Social-Contrastive Self-Modeling",
  "summary": "SEAA replaces static prompts and passive text memory with an editable HMM state-transition matrix encoding behavioral inertia, updated by a reflexive metacognition loop and social comparison among agents. Costs include added architectural complexity, extra LLM calls for reflection, and a state-update mechanism whose stability must be maintained, with no claim of task-performance improvement. It can fail through oscillating or collapsing HMM state transitions, unbounded personality drift in the opposite direction, and self-narratives that are artifacts of prompting rather than functional behavior; it addresses research questions about emergent agent identity, not production reliability or capability.",
  "rationale": "The paper is an exploratory, falsifiable-hypothesis study of artificial-self emergence with a prototype and small hosted-LLM experiments, offering no quantified production gain and no demonstrated benefit for constrained-infrastructure deployments.",
  "technique": "Behavior-inertia HMM agent architecture with reflexive self-modeling",
  "family": "orquestracao_multiagente",
  "recommendation": "nao_aplica",
  "published": "2026-09-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.17331",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "source_url": "https://arxiv.org/abs/2609.17331",
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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