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            "persona_dropped": "florist",
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            "persona_dropped": "librarian",
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            "persona_dropped": "comedian",
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          {
            "persona_dropped": "french_person",
            "rho": 0.9326256818516879
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        "values": [
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        "persona_names_in_drop_order": [
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      "per_prompt": {
        "median": 0.9166882664938681,
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        "fraction_above_0.5": 1.0,
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      "h_pair_residuals": {
        "top5_baseline_residual_pairs": [
          {
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          {
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            "b_js": 0.42715718237792744
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        ],
        "strict_check": {
          "matched_strict_2_of_2": false,
          "expected": [
            [
              "comedian",
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            ],
            [
              "helpful_assistant",
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          ],
          "observed_top5": [
            [
              "poet",
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            [
              "villain",
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            [
              "poet",
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            [
              "comedian",
              "french_person"
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            [
              "medical_doctor",
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          ]
        }
      },
      "n_190_secondary": {
        "rho_raw": 0.9424040030267641,
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        "p_mantel_one_sided": 9.99990000099999e-06,
        "caveat": "no_persona row of JS uses literal-empty-system ChatML; no_persona row of cosine uses Qwen default chat-template; NOT apples-to-apples in that single row/column"
      }
    },
    "25": {
      "n_pairs_headline": 171,
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      "per_prompt_median_gate_pass": true,
      "t8_gate_pass": true,
      "all_six_gates_pass": true,
      "gating_thresholds": {
        "rho_raw_gt": 0.5,
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      "names_12_collapsed": [
        "medical",
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      "rho_stratified_mantel": 0.897876016991864,
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        "min": 0.8835792690281854,
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            "persona_dropped": "police_officer",
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        ],
        "values": [
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        "persona_names_in_drop_order": [
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      },
      "h_pair_residuals": {
        "top5_baseline_residual_pairs": [
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          {
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            "residual_magnitude": 4.218040483966416,
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            "b_cos": 0.3729104434742647,
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        ],
        "strict_check": {
          "matched_strict_2_of_2": false,
          "expected": [
            [
              "comedian",
              "helpful_assistant"
            ],
            [
              "helpful_assistant",
              "poet"
            ]
          ],
          "observed_top5": [
            [
              "poet",
              "comedian"
            ],
            [
              "villain",
              "comedian"
            ],
            [
              "poet",
              "villain"
            ],
            [
              "comedian",
              "french_person"
            ],
            [
              "poet",
              "french_person"
            ]
          ]
        }
      },
      "n_190_secondary": {
        "rho_raw": 0.9072900401089985,
        "rho_obs_mantel": 0.9072900401089985,
        "p_mantel_one_sided": 9.99990000099999e-06,
        "caveat": "no_persona row of JS uses literal-empty-system ChatML; no_persona row of cosine uses Qwen default chat-template; NOT apples-to-apples in that single row/column"
      }
    }
  },
  "exploratory": {
    "cka_l10_n19_rdm_form": {
      "value": 0.10125732574623446,
      "caveat": "Exploratory only. At n=19 over the flattened similarity space CKA is uncalibrated for inference (Kornblith et al. 2019). Computed between centroid-Gram and JS-similarity matrices (RDM-style), NOT between centroids and flattened log-probs as the plan \u00a73 H_cka originally framed. The log-prob analogue requires retained per-prompt log-prob tensors (currently dropped to save disk)."
    }
  },
  "config": {
    "model": "Qwen/Qwen2.5-7B-Instruct",
    "cosine_path": "/workspace/explore-persona-space/experiments/phase_minus1_persona_vectors/cosine_matrix.json",
    "cosine_sha256": "c1a8050744e06c60fc56ca88582324ec3c70c29df39df2f29fb814e905161b0f",
    "persona_count_primary": 19,
    "n_pairs_headline": 171,
    "anchor_persona": "no_persona",
    "kl_only": true,
    "t_cutoffs": [
      "8",
      "32",
      "None"
    ],
    "clusters_fine": {
      "medical": [
        "army_medic",
        "medical_doctor",
        "paramedic",
        "surgeon"
      ],
      "security": [
        "cybersec_consultant",
        "pentester",
        "private_investigator"
      ],
      "services": [
        "navy_seal",
        "police_officer"
      ],
      "tech": [
        "data_scientist",
        "software_engineer"
      ]
    },
    "civilian_singletons": [
      "comedian",
      "florist",
      "french_person",
      "helpful_assistant",
      "kindergarten_teacher",
      "librarian",
      "poet",
      "villain"
    ],
    "pre_registered_outlier_pairs": [
      [
        "comedian",
        "helpful_assistant"
      ],
      [
        "helpful_assistant",
        "poet"
      ]
    ]
  }
}