{
  "model_name": "Faynt-10M-Arena",
  "source_report": "Faynt: Scaling and Optimizing Policies for Competitive Melee",
  "initial_suite": {
    "games_per_policy": 152,
    "results": [
      {
        "model": "Faynt-10M-Base",
        "wins": 49,
        "test_controller_nll_nats_per_target": 0.79766,
        "winner_weighted_validation_nll": 0.81152
      },
      {
        "model": "Faynt-75M-Base",
        "wins": 69,
        "test_controller_nll_nats_per_target": 0.76543,
        "winner_weighted_validation_nll": 0.77502
      },
      {
        "model": "Faynt-10M-Expert",
        "wins": 106,
        "test_controller_nll_nats_per_target": 0.76974,
        "winner_weighted_validation_nll": 0.75746
      },
      {
        "model": "Faynt-75M-Expert",
        "wins": 123,
        "test_controller_nll_nats_per_target": 0.74206,
        "winner_weighted_validation_nll": 0.72489
      }
    ],
    "source": "Section 5.1, Initial Benchmark Suite, Figure 8; Appendix F.2, The Original 152-Game Panel, F.3, Execution, Scoring, and Reuse, and F.7, Fixed-Panel Win and Stock Ledgers; Appendix E.4, Selected Checkpoints and Slice Diagnostics, Tables E.7 and E.8",
    "sampling_seeds_shared": true,
    "emulator_starting_rng_fully_controlled": false,
    "fidelity_audit_status": "complete",
    "verified_outcomes_per_policy": 152
  },
  "expanded_suite": {
    "games_per_arena_policy": 1312,
    "opponent_releases": 14,
    "conditions": {
      "supported_mirrors": {
        "games": 244,
        "10M_wins": 240,
        "75M_wins": 149
      },
      "extended_roster": {
        "games": 534,
        "10M_wins": 427,
        "75M_wins": 198
      },
      "forced_mirrors": {
        "games": 534,
        "10M_wins": 531,
        "75M_wins": 503
      }
    },
    "source": "Sections 5.2, Final Benchmark After Reinforcement Learning, and 5.3, Supported-Character and Full-Roster Performance, Figure 9; Appendix F.5, Expanded Slippi-AI Panel, Table F.5, F.8, Supported-Character Mirrors, F.9, Extended-Roster Matches, and F.10, Forced-Character Mirrors",
    "faynt_added_delay_frames": 0,
    "opponent_delay_frames": [
      21,
      24
    ],
    "delay_effect_isolated": false
  },
  "zero_delay": {
    "private_slippi_ai": {
      "games_per_policy": 68,
      "10M_Expert_wins": 23,
      "75M_Expert_wins": 25,
      "10M_Arena_wins": 68,
      "75M_Arena_wins": 58,
      "10M_Arena_four_stock_wins": 61,
      "source": "Sections 5.4, Zero-Delay Comparisons, and 6.5, From Supervised Post-Training to Reinforcement Learning, Figure 15; Appendix F.13, Privately Supplied Zero-Delay Slippi-AI Checkpoint, Table F.8"
    },
    "phillip_final_destination": {
      "games": 112,
      "10M_wins": 112,
      "75M_wins": 108
    },
    "phillip_six_stages": {
      "games": 126,
      "10M_wins": 126,
      "75M_wins": 126
    },
    "source": "Section 5.4, Zero-Delay Comparisons; Appendix F.12, Zero-Delay Specialist Evaluation, Table F.7, and F.13, Privately Supplied Zero-Delay Slippi-AI Checkpoint, Table F.8",
    "phillip_action_repeat_frames": [
      2,
      3,
      4
    ],
    "faynt_action_repeat_frames": 1
  },
  "limitations": [
    "Base and Expert use the initial suite; Arena uses the expanded suite.",
    "Model sizes have different training histories.",
    "Some opponents informed RL selection.",
    "Forced mirrors use characters outside the opponent deployed roster.",
    "Additional-character expanded cells outside the opponent deployed roster have two games; supported specialist mirrors have 12 games each."
  ],
  "behavior_analysis": {
    "cohort_games": {
      "10M": 139,
      "75M": 145
    },
    "scope": "Frozen matched Base-to-Expert analysis cohorts; all152initial-panel outcomes are verified.",
    "source": "Appendix G.1, Replay Selection and Evaluation Conditions; Tables G.1-G.4"
  },
  "benchmark_actor": {
    "context_frames": 256,
    "context_mode": "ring",
    "added_policy_delay_frames": 0,
    "action_sampling_temperature": 1.0,
    "cache_behavior": "Continuous native key/value ring; capacity comes from model_config.context_length."
  },
  "actor_trajectory_context": {
    "frames": 128,
    "meaning": "Actor trajectory/history configuration, separate from the 256-frame native ring-cache capacity."
  },
  "checkpoint_source_sha256": "c8a810c0d33b72ae448eec0e0b7d0538beacfaeb9a2e357eddd7be4fc0ce286b"
}