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          {
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        ]
      }
    }
  ],
  "ranking": [
    {
      "policy_id": "policy_bc_v4",
      "human_likeness": 3.809274771888009e-05,
      "fool_rate_at_0.5": 0.0,
      "detection_auc": 1.0,
      "transfer_human_likeness": 7.310015739154003e-05
    },
    {
      "policy_id": "policy_bc_v2",
      "human_likeness": 2.2638500048010705e-05,
      "fool_rate_at_0.5": 0.0,
      "detection_auc": 1.0,
      "transfer_human_likeness": 1.5289149273327188e-05
    },
    {
      "policy_id": "policy_bc_v4_1",
      "human_likeness": 1.838149538810249e-05,
      "fool_rate_at_0.5": 0.0,
      "detection_auc": 1.0,
      "transfer_human_likeness": 7.074711287262004e-05
    },
    {
      "policy_id": "policy_bc_v3",
      "human_likeness": 1.3073038784412534e-05,
      "fool_rate_at_0.5": 0.0,
      "detection_auc": 1.0,
      "transfer_human_likeness": 1.3331757304089826e-05
    }
  ],
  "checkpoint": "models/style_discriminator/style_ensemble.pkl",
  "lessons": [
    "The realism scorer said policy rollouts look real (0.85 vs 0.87); against actual policy negatives the same feature set separates them perfectly. A discriminator is only as strong as its negatives.",
    "Top tells: alt_rate, in_single, frac_triple, in_overtime, frac_gap_lt_1. These are what a policy must fix to become statistically human.",
    "Several top tells come from the rollout harness (strict side alternation, fixed 40-event continuation), not the policy. Removing all 13 harness features still leaves AUC 1.000, so the style gap is real either way.",
    "Detection is not a close call, so mean P(human) \u2014 not accuracy \u2014 is the ranking signal while every policy sits at AUC 1.0.",
    "The judge needs roughly 8 generated actions before it is confident, so short bursts of policy play are much harder to spot than full continuations.",
    "Dropping the 60 strongest tells still leaves near-perfect detection: the AI signature is distributed across the whole feature set, not one fixable bug."
  ],
  "notes": "Binary style discriminator: P(sequence is human) from action-sequence statistics. Negatives are offline policy rollouts starting from real battle prefixes."
}