{
  "id": "EXP-2026-0001",
  "kind": "experiment",
  "label": "AER-0 MVP v0.1 closure: are the invariants executable?",
  "created_at": "2026-09-08",
  "updated_at": "2026-09-08",
  "values": {
    "eml_status": "STABLE",
    "eml_evidence_level": "E2",
    "eml_object_version": "0.1",
    "eml_canonical_url": "https://evemisslab.com/ai/experiments/EXP-2026-0001/",
    "eml_provenance": {
      "source": "EveMissLab research collection: Adaptive Epistemic Systems (真本體論13)",
      "extracted_by": "Splice (Claude Code), reading the canonical UTF-8 sources and each lab's own result reports",
      "extracted_at": "2026-09-11",
      "generator": "tools/extract_aes/extract.py",
      "claim_boundary": "status, evidence level and result type follow the source artifact's own stated claim boundary; nothing is upgraded beyond what the report supports"
    },
    "eml_summary": "The approved Python + SQLite runtime was built under mssp-tdd-apr and closed on behavioural, structural and discriminative witnesses: candidate gate, provenance validation, stale-version conflict, stable-vs-volatile scheduling, shock activation, verifier rejection, workflow reuse/adapt/create, model swap, model-free core, end-to-end verified commit. 25 tests; the refresh benchmark selected 2 nodes under fixed TTL versus 1 volatile node under tension scheduling; a clean extracted replay of the sealed archive passed before release.",
    "eml_summary_zh": "核准的 Python + SQLite runtime 在 mssp-tdd-apr 下建成，並以行為、結構、判別三類見證收束：candidate gate、provenance 驗證、過期版本衝突、穩定 vs 易變排程、shock 觸發、verifier 拒絕、workflow reuse／adapt／create、模型替換、無模型核心、端到端已驗證 commit。25 個測試；refresh benchmark 在固定 TTL 下選了 2 個節點、在張力排程下只選 1 個易變節點；密封封存包乾淨解壓重播通過後才發布。",
    "eml_label_zh": "AER-0 MVP v0.1 收束：不變量能不能被執行？",
    "eml_primary_domain": "AI Architecture",
    "eml_domains": [
      "Evaluation",
      "Agent Systems"
    ],
    "eml_program_id": "PRG-2026-0001",
    "eml_hypothesis": "The scoped MVP invariants (canonical state ownership, candidate gating, provenance, versioned commit, selective refresh, capability/container separation, workflow reuse, model replaceability) can be implemented with positive and falsifying executable witnesses.",
    "eml_metrics": {
      "tests_passed": 25,
      "refresh_benchmark": {
        "fixed_6h_ttl_nodes_refreshed": 2,
        "aer_tension_nodes_refreshed": 1
      },
      "closure": {
        "behavioral": "PASS",
        "structural": "PASS",
        "discriminative": "PASS",
        "independent_twin": "NotMeasured",
        "production_readiness": "NotClaimed",
        "general_ai_superiority": "NotMeasured"
      }
    },
    "eml_interpretation": "A mechanism closure, not a claim of AI superiority: the declared invariants exist in code, are exercised by falsifying witnesses, and survive a fresh-process replay.",
    "eml_limitations": [
      "DEGRADED-TWIN: only one live execution context; no simulated independent verdict claimed.",
      "Not measured: performance against production agent frameworks, live research accuracy, distributed semantics, security hardening, real heterogeneous backends, multi-day drift."
    ],
    "eml_run_count": 1,
    "eml_result_type": "POSITIVE",
    "eml_procedure": "TDD under mssp-tdd-apr; full suite, research-assistant demo and refresh benchmark run before packaging; checksum-verified clean extraction replayed after sealing.",
    "eml_software_environment": "Python 3.11+, SQLite; no network, no external database, no LLM API required.",
    "eml_reproduction_instructions": "Extract the round's FINAL bundle; python -m pytest -q; python -m examples.research_assistant_demo; python -m benchmarks.<round benchmark>. Checksums in SHA256SUMS.txt.",
    "eml_completed_at": "2026-09-08",
    "eml_data_basis": "DETERMINISTIC RUNTIME",
    "eml_authors": [
      "Neo.K (EveMissLab)"
    ],
    "eml_ai_collaborators": [
      "Sol (GPT-5.6, OpenAI ChatGPT)"
    ]
  },
  "canonical_url": "https://evemisslab.com/ai/experiments/EXP-2026-0001/",
  "json": "/ai/experiments/EXP-2026-0001/index.json",
  "relations": [
    {
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      "predicate": "runs_on",
      "source": "EXP-2026-0001",
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      "predicate": "uses_benchmark",
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    },
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      "id": "REL-2026-0101",
      "predicate": "tests",
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    },
    {
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      "predicate": "tests",
      "source": "EXP-2026-0001",
      "target": "THY-2026-0001",
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    {
      "id": "REL-2026-0103",
      "predicate": "tests",
      "source": "EXP-2026-0001",
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      "id": "REL-2026-0104",
      "predicate": "produced",
      "source": "EXP-2026-0001",
      "target": "ART-2026-0014",
      "status": "ACTIVE"
    },
    {
      "id": "REL-2026-0110",
      "predicate": "extends",
      "source": "EXP-2026-0002",
      "target": "EXP-2026-0001",
      "status": "ACTIVE"
    }
  ],
  "snapshot": {
    "snapshot_id": "AI-SNAPSHOT-v0.1-fe85b9694a45",
    "created_at": "2026-09-11T05:00:27Z",
    "format_version": "0.1",
    "sedb_baseline": "v0.4B contract; static source content/ai/",
    "generator_version": "evemisslab-com ai_research 0.1",
    "object_count": 124,
    "relation_count": 499,
    "artifact_count": 58
  }
}