{
  "id": "EXP-2026-0011",
  "kind": "experiment",
  "label": "PACC-Lab v0.4 — correlated sources and dependence geometry",
  "created_at": "2026-09-09",
  "updated_at": "2026-09-09",
  "values": {
    "eml_status": "STABLE",
    "eml_evidence_level": "E3",
    "eml_object_version": "0.1",
    "eml_canonical_url": "https://evemisslab.com/ai/experiments/EXP-2026-0011/",
    "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": "Sources are grouped into clusters with a latent shared inversion; the correct reference uses the joint likelihood and a naive independent-product Bayes is the negative control (joint beats naive in moderate and high geometry; gap exactly 0 in the independent geometry). Non-probabilistic systems see only cluster membership. Task-state convergence survives: N0/N1/N3 pass in moderate and high correlation (N2 drops to agreement 0.8364 in high). The latent dependence coordinate — the posterior that the cluster is in its corrupted branch — is not recovered by a train-only affine-sigmoid map from any system: real JS ≈ shuffled ≈ constant.",
    "eml_summary_zh": "來源分成叢集並帶潛在共同反轉；正確的參考用聯合概似，天真的獨立乘積 Bayes 是負控制（在中、高相關幾何下聯合勝過天真；獨立幾何下差距恰為 0）。非概率系統只看得到叢集歸屬。任務狀態收斂存活：N0/N1/N3 在中、高相關下通過（N2 在高相關下一致度掉到 0.8364）。潛在依賴座標——叢集處於受污染分支的後驗——任何系統的 train-only affine-sigmoid 映射都無法重建：真實 JS ≈ shuffled ≈ constant。",
    "eml_label_zh": "PACC-Lab v0.4——相關來源與依賴幾何",
    "eml_primary_domain": "Model Representation",
    "eml_domains": [
      "Evaluation",
      "Formal AI"
    ],
    "eml_program_id": "PRG-2026-0001",
    "eml_hypothesis": "Task-state convergence survives dependent evidence, and the non-probabilistic relation state itself maps to the Bayesian common-cause posterior.",
    "eml_metrics": {
      "verdict": "TASK-LEVEL CONVERGENCE SURVIVES DEPENDENT EVIDENCE; LATENT DEPENDENCE-STATE CONVERGENCE NOT SUPPORTED",
      "environment": {
        "joint_vs_naive_logloss": {
          "independent": [
            1.245687,
            1.245687
          ],
          "moderate": [
            1.632599,
            1.834445
          ],
          "high": [
            1.758082,
            2.018617
          ]
        }
      },
      "task": {
        "moderate": {
          "N0": {
            "agreement": 0.955247,
            "D_R": 0.008095
          },
          "N3": {
            "agreement": 0.942901,
            "D_R": 0.008234
          }
        },
        "high": {
          "N0": {
            "agreement": 0.861111,
            "D_R": 0.009827
          },
          "N2": {
            "agreement": 0.83642,
            "D_R": 0.017874
          }
        }
      },
      "dependence_coordinate": {
        "moderate_real_js": 0.0605,
        "moderate_shuffled": 0.0641,
        "moderate_constant": 0.0615,
        "high_real_js": 0.0398,
        "high_shuffled": 0.04,
        "high_constant": 0.0397,
        "pass": 0
      }
    },
    "eml_interpretation": "A layered picture: a decision-relevant quotient state can converge to a probabilistic coordinate while a deeper latent explanatory variable stays representation-dependent — evidence against the strongest 'everything becomes probability-like' reading.",
    "eml_limitations": [
      "A result about the current state representation and frozen readout, not a theorem that no richer relation graph could encode the latent variable.",
      "Cluster membership is given, not learned."
    ],
    "eml_random_seeds": [
      "20260909",
      "6 secondary seeds",
      "4 high-correlation seeds at primary scale"
    ],
    "eml_run_count": 3,
    "eml_result_type": "MIXED",
    "eml_controls": [
      "shuffled-target mapping",
      "constant prediction",
      "broken composition (v0.6+)",
      "target-local refit (v0.7+)"
    ],
    "eml_software_environment": "Python; deterministic seeded generators; no network, no LLM.",
    "eml_reproduction_instructions": "Extract the version's FINAL bundle; python -m pytest -q; run the version's primary script with the recorded seed; docs/PACC_LAB_v0.N_RESULTS.md and docs/EXPERIMENT_PROTOCOL_v0.N.md are inside the bundle.",
    "eml_completed_at": "2026-09-09",
    "eml_data_basis": "SYNTHETIC",
    "eml_model_ids": [
      "MOD-2026-0001",
      "MOD-2026-0002",
      "MOD-2026-0003",
      "MOD-2026-0004",
      "MOD-2026-0005"
    ],
    "eml_dataset_ids": [
      "DAT-2026-0001"
    ],
    "eml_benchmark_ids": [
      "BEN-2026-0001"
    ],
    "eml_authors": [
      "Neo.K (EveMissLab)"
    ],
    "eml_ai_collaborators": [
      "Sol (GPT-5.6, OpenAI ChatGPT)"
    ]
  },
  "canonical_url": "https://evemisslab.com/ai/experiments/EXP-2026-0011/",
  "json": "/ai/experiments/EXP-2026-0011/index.json",
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  "snapshot": {
    "snapshot_id": "AI-SNAPSHOT-v0.1-fe85b9694a45",
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